CN113496220A

CN113496220A - Image processing method, system and computer readable storage medium

Info

Publication number: CN113496220A
Application number: CN202111040819.7A
Authority: CN
Inventors: 杨喆; 王志斌; 李�昊
Original assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Current assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Priority date: 2021-09-07
Filing date: 2021-09-07
Publication date: 2021-10-12

Abstract

The invention discloses an image processing method, an image processing system and a computer readable storage medium. Wherein, the method comprises the following steps: acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result. The invention solves the technical problem that the classification result is inaccurate because the remote sensing image is subjected to ground feature classification processing by adopting a classification change detection method in the prior art.

Description

Image processing method, system and computer readable storage medium

Technical Field

The present invention relates to the field of image processing, and in particular, to an image processing method, system, and computer-readable storage medium.

Background

At present, methods for analyzing and processing remote sensing images by using algorithms such as deep learning and the like and extracting valuable information in the images are widely applied to the field of image processing and are mainly realized by a classification change detection mode.

However, the existing multi-classification change detection mode has the defects of higher data acquisition difficulty and fixed change pattern spot types, and cannot better meet the flexible requirements of users on the pattern spot types.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the invention provides an image processing method, an image processing system and a computer readable storage medium, which are used for at least solving the technical problem that in the prior art, a classification change detection method is adopted to classify ground objects of remote sensing images, so that the classification result is inaccurate.

According to an aspect of the embodiments of the present invention, there is provided an image processing method, including: acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

According to an aspect of the embodiments of the present invention, there is provided an image processing method, including: acquiring a first satellite image and a second satellite image of a coastal local agricultural area; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining the type of the change pattern spot in the first satellite image as the cultivated land type based on the first ground feature classification result, and determining the type of the change pattern spot in the second satellite image as the greenhouse type based on the second ground feature classification result; determining the change type of the change pattern spot to be changed from the cultivated land type to the greenhouse type.

According to an aspect of the embodiments of the present invention, there is provided an image processing method, including: acquiring a first satellite image and a second satellite image in a local construction area of a city; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining a change pattern spot type of the change pattern spot in the first satellite image as a building type based on the first feature classification result, and determining a change pattern spot type of the change pattern spot in the second satellite image as a park type based on the second feature classification result; determining a change type of the change pattern spot to be changed from the building type to the park type.

According to an aspect of the embodiments of the present invention, there is provided an image processing method, including: acquiring a first satellite image and a second satellite image in a local drainage basin region of a river channel; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining a change pattern spot type of the change pattern spot in the first satellite image as a river type based on the first feature classification result, and determining a change pattern spot type of the change pattern spot in the second satellite image as a scenic spot type based on the second feature classification result; determining a change type of the change pattern spot to be changed from the river type to the scenic spot type.

According to an aspect of the embodiments of the present invention, there is provided an image processing method, including: receiving a first image and a second image within a target area range from a client; respectively carrying out feature classification processing on the first image and the second image to obtain a first feature classification result and a second feature classification result, carrying out change detection processing on the first image and the second image to obtain a change pattern patch, and determining a change pattern patch type to which the change pattern patch belongs based on the first feature classification result and the second feature classification result; and feeding back the type of the change pattern spot to the client.

According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, including: a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform any of the above-described image processing methods.

According to another aspect of the embodiments of the present invention, there is also provided a processor, including: the processor is configured to execute a program, wherein the program executes any one of the image processing methods.

According to another aspect of the embodiments of the present invention, there is also provided an image processing system, including: a processor; and a memory, connected to the processor, for providing instructions to the processor for processing the following processing steps: acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

In the embodiment of the invention, an image processing mode is adopted, and a first image and a second image in a target area range are obtained; the first image is an image obtained by shooting the target area range at a first time, the second image is an image obtained by shooting the target area range at a second time, and the second time is later than the first time, namely the first image and the second image respectively correspond to a front-stage image and a rear-stage image of the target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result. The method achieves the purpose of acquiring the multi-classification change pattern spots in a mode of combining classification change detection and surface feature classification processing, thereby achieving the technical effect of improving the accuracy of the classification result of surface feature classification based on the remote sensing image, and further solving the technical problem of inaccurate classification result caused by model training and data labeling when the remote sensing image is classified by adopting a classification change detection method in the prior art.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

fig. 1 is a block diagram of a hardware structure of an alternative computer terminal (or mobile device) for implementing an image processing method according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating an image processing method according to an embodiment of the present invention;

FIG. 3 is a flowchart of an alternative image processing method according to an embodiment of the present invention;

FIG. 4 is a schematic diagram illustrating a comparison of an alternative first image and a second image according to an embodiment of the invention;

FIG. 5a is a schematic diagram of an alternative first presentation showing types of tillable areas in accordance with an embodiment of the present invention;

FIG. 5b is a schematic diagram of an alternative embodiment of the present invention showing the type of water area in a second display mode;

FIG. 6 is a schematic diagram of an alternative binary variation pattern according to an embodiment of the present invention;

FIG. 7 is a flowchart of an alternative image processing method according to an embodiment of the present invention;

FIG. 8 is a diagram illustrating an alternative image processing scenario according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of an alternative image processing scenario according to an embodiment of the present invention;

FIG. 10 is a flowchart illustrating another image processing method according to an embodiment of the present invention;

FIG. 11 is a flowchart illustrating another image processing method according to an embodiment of the present invention;

FIG. 12 is a flowchart illustrating another image processing method according to an embodiment of the present invention;

fig. 13 is a schematic diagram illustrating image processing performed at a cloud server according to an embodiment of the present invention;

FIG. 14 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present invention;

fig. 15 is a block diagram of a computer terminal according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The processing of obtaining, storing, applying and the like of the personal information of the user related to the embodiment of the application all accords with the regulation of related laws and regulations and does not violate the good custom of the public order.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

First, some terms or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:

the remote sensing intelligent analysis system is a system for analyzing and processing remote sensing images by utilizing algorithms such as deep learning and the like and extracting valuable information in the images, and one set of the system can carry various remote sensing analysis capabilities such as binary change detection, ground feature classification, building extraction and the like.

And (3) land feature classification, namely classifying each pixel unit in the remote sensing image according to a land feature type, wherein the general classification type can comprise types or types of cultivated land, water area, building, road, structure, forest land, grassland, bare land and the like.

Change detection: the method is characterized in that two-stage images of the same area at different time are processed, and a changed image spot is extracted. The binary classification change detection can only extract the position of the change pattern spot, and cannot determine the type of the change pattern spot. The multi-classification change detection can extract the change type of the image spot while extracting the position of the change image spot.

It should be noted that, in the art, the classification change detection is mainly divided into two types: two-class change detection and multi-class change detection. Wherein, the binary classification change detection can only extract the position of the change pattern spot, and can not determine the type of the change pattern spot; the multi-classification change detection can extract the change type of the pattern spots while extracting the positions of the change pattern spots, but the adoption of a multi-classification change detection mode needs model training and data labeling, so that the data acquisition difficulty is higher, the change pattern spot type is fixed, and the flexible requirements of users on the pattern spot type cannot be better met.

Example 1

There is also provided, in accordance with an embodiment of the present invention, a method embodiment of image processing, it should be noted that the steps illustrated in the flowchart of the accompanying drawings may be implemented in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.

The method provided by the embodiment 1 of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Fig. 1 shows a block diagram of a hardware structure of a computer terminal (or mobile device) for implementing an image processing method. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …, 102 n) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors 102 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the image processing method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the image processing method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

Under the operating environment, the present application provides an image processing method as shown in fig. 2. Fig. 2 is a flowchart of an image processing method according to an embodiment of the present invention, as shown in fig. 2, the image processing method includes the following steps:

step S202, acquiring a first image and a second image within a target area range;

step S204, respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result;

step S206, change detection processing is carried out on the first image and the second image to obtain a change pattern spot;

step S208, determining a change pattern type to which the change pattern belongs based on the first feature classification result and the second feature classification result.

The image processing method provided in the embodiment of the present application may be applied, but not limited to, in a remote sensing intelligent analysis system, and is configured to perform feature classification processing and change detection processing based on an acquired remote sensing image in a target area range, and determine a change pattern type to which a change pattern belongs in the remote sensing image.

As an optional embodiment, the image processing method provided by the present application may be applied to the meteorological field (e.g., cloud layer extraction, meteorological forecast, meteorological warning, etc.); natural resource and ecological environment fields (e.g., weather forecast, change detection, ecological redline change detection, multi-classification change detection, ground feature classification, greenhouse extraction, road network extraction, building change detection (satellite, unmanned aerial vehicle), etc.), water conservancy fields (e.g., water area change detection, greenhouse extraction, water body extraction (optical, radar), sheet forest extraction, cage culture extraction, sand pit extraction, river house extraction, barrage extraction, photovoltaic power plant extraction, etc.), agroforestry fields (e.g., crop extraction (wheat, rice, potato, etc.), unmanned aerial vehicle crop identification (corn, flue-cured tobacco, myotonia, etc.), land parcel identification, growth monitoring (index calculation), agricultural assessment, pest monitoring, planting suggestion pushing, etc.), secondary disaster fields (e.g., disaster monitoring, travel disaster warning, etc.), life services, Take-out, logistics) areas (e.g., travel route planning, travel advice pushing, personnel mobilization, price adjustment, etc.); the city planning field (e.g., road network extraction (satellite, drone), building extraction, building change detection (satellite, drone), fire protection, etc.).

Optionally, the first video is a video captured of the target area range at a first time, the second video is a video captured of the target area range at a second time, and the second time is later than the first time.

Optionally, the surface feature classification result may include a plurality of surface feature types, and the surface feature types may include, but are not limited to: water area type, building type, road type, structure type, woodland type, grass type, bare land type, etc.

Optionally, in this embodiment of the application, the type of the change pattern spot to which the change pattern spot belongs is determined based on the first feature classification result and the second feature classification result, for example, the first feature classification result is a cultivated land type, the second feature classification result is a water area type, and since the feature classification result in the target area range is changed from the cultivated land type to the water area type, that is, cultivated land in the target area range does not exist, it may be indicated that the change pattern spot does not belong to cultivated land loss.

In an alternative embodiment, the image types of the first image and the second image include one of: satellite images, aerial images.

Optionally, in the embodiment of the present invention, the image types of the first image and the second image may be determined according to, but not limited to, different image applications or different image capturing manners.

It should be noted that, the ground feature classification is used as a remote sensing capability of the basis of the remote sensing intelligent analysis system, and can be conveniently combined with other remote sensing analysis capabilities to obtain a better classification effect. In the actual use process, the binary classification change detection can detect various types of change patches and only extract the positions of the change patches, but different users may only be interested in some types of change patches and not regard the change patches as changes. Therefore, the image spots extracted by the binary classification change detection need to be further classified so as to ensure that the user can flexibly acquire the corresponding image spot types according to actual needs.

According to the embodiment of the invention, the attributes of the front-stage and rear-stage change patterns extracted by the two-stage change detection are assigned by utilizing the ground feature classification information, and the change pattern detection and the pattern classification are decoupled, so that the positions of the change patterns can be extracted, the corresponding pattern types can be flexibly obtained according to actual requirements, the technical effect of flexibly and quickly realizing multi-classification change detection without additional data marking and model development can be realized, and the technical problem that the classification result is inaccurate when a classification change detection method is adopted to classify remote sensing images in the prior art is solved.

As an alternative embodiment, fig. 3 is a flowchart of an alternative image processing method according to an embodiment of the present invention, and as shown in fig. 3, the obtained early-stage image and the obtained late-stage image (i.e., the first image and the second image) within the target area are subjected to feature classification and extraction to obtain feature classification results of the early-stage image and the late-stage image; then, performing two-classification change detection processing on the early-stage image and the later-stage image to obtain two-classification change patterns of the early-stage image and the later-stage image; determining an early image feature type and a later image feature type corresponding to the two-classification change pattern spots based on the early image feature classification result and the later image feature classification result corresponding to the early image and the later image; the method comprises the steps of inquiring the change condition of the ground feature type of the image in the early and late periods corresponding to the two-class change pattern spots, assigning the two-class change pattern spot type to which the two-class change pattern spot belongs based on the change condition of the ground feature type, and finally realizing the multi-class two-class change pattern spot extraction operation.

It is easy to note that in the embodiment of the present invention, the first image is an image obtained by capturing the target area range at a first time, the second image is an image obtained by capturing the target area range at a second time, and the second time is later than the first time, that is, the first image and the second image respectively correspond to a previous image and a later image of the target area range; respectively carrying out ground feature classification extraction on the first image and the second image to obtain ground feature classification results of the first image and the second image; then, carrying out change detection processing on the first image and the second image to obtain change pattern spots of the first image and the second image, wherein the change detection processing is classified change detection processing, and the change pattern spots are classified change pattern spots; and determining the type of the change pattern spots attributed to the two classification change pattern spots based on the feature classification results of the first image and the second image.

Therefore, the method and the device achieve the purpose of acquiring the multi-classification change pattern spots in a mode of combining classification change detection and surface feature classification processing, achieve the technical effect of improving the accuracy of the classification result of surface feature classification based on the remote sensing image, and further solve the technical problem that the classification result is inaccurate when a classification change detection method is adopted to perform surface feature classification processing on the remote sensing image in the prior art.

In an optional embodiment, the performing a feature classification process on the first image to obtain the first feature classification result includes: and classifying each pixel unit in the first image according to the feature type corresponding to each pixel unit in the first image to obtain the first feature classification result.

In this embodiment, the feature classification processing on the first image may be to perform classification processing on each pixel unit in the first image to obtain the first feature classification result, where the first feature classification result is used to determine a change patch type to which the change patch belongs.

In an optional embodiment, the performing a feature classification process on the second image to obtain the second feature classification result includes: and classifying each pixel unit in the second image according to the feature type corresponding to each pixel unit in the second image to obtain the second feature classification result.

In this embodiment, the feature classification processing on the second image may be to perform classification processing on each pixel unit in the second image to obtain the second feature classification result, where the second feature classification result is used to determine the change patch type to which the change patch belongs.

As an alternative embodiment, fig. 4 is a schematic diagram illustrating a comparison between an alternative first image and an alternative second image according to an embodiment of the present invention, and it can be determined that there is a difference between the first image and the second image. And respectively performing feature classification processing on the first image and the second image based on the first image and the second image to obtain a first feature classification result and a second feature classification result, wherein fig. 5a and 5b show schematic diagrams of feature classification results respectively corresponding to the first image and the second image, which are selectable, and when it is determined that the feature classification result of the target area changes, the feature classification results are respectively displayed in different display modes.

In an optional embodiment, performing change detection processing on the first image and the second image to obtain the change pattern spot includes: and performing classification change detection processing on the first image and the second image to extract a classification change pattern spot.

As an alternative embodiment, fig. 6 shows a schematic diagram of two classification variation patterns of an alternative two-classification detection result, as shown in fig. 6, a variation pattern exists in the target area, and the variation pattern includes a variation area in the first land feature classification result and the second land feature classification result, that is, an area of the first land feature classification result, which is changed from arable land to water area.

As an alternative embodiment, fig. 7 is a flowchart of an alternative image processing method according to an embodiment of the present invention, and as shown in fig. 7, the determining the type of the variation pattern attributed to the variation pattern based on the first feature classification result and the second feature classification result includes:

step S302, determining a first feature type corresponding to the change pattern spot based on the first feature classification result;

step S304, determining a second ground object type corresponding to the change pattern spot based on the second ground object classification result;

step S306, determining the change of the feature type by using the first feature type and the second feature type;

and step S308, determining the type of the change pattern spot to which the change pattern spot belongs according to the type change of the ground object.

Optionally, the first feature type and the second feature type corresponding to the variation pattern are the feature types with the largest number of pixels of the variation pattern (i.e. the largest pixel area) in the first feature classification result and the second feature classification result, respectively.

In this embodiment, the change pattern spot type is determined by a first ground object type and a second ground object type, for example, if the first ground object type is a water area, and the second ground object type is a building, the change pattern spot type is a water area to building. And when the first ground feature type is consistent with the second ground feature type, the change pattern spot is considered to be a pseudo change, and the change pattern spot is deleted and not kept.

As an alternative embodiment, fig. 8 is a schematic diagram of an alternative image processing according to an embodiment of the present invention, as shown in fig. 8, after obtaining the first feature classification result, the second feature classification result, and the second classification variation pattern, respectively nesting the two classification variation patterns into the first feature classification result and the second feature classification result, and determining a first feature type and a second feature type corresponding to the two classification variation patterns based on the nested first feature classification result and the second feature classification result, taking the feature type with the largest pixel area of the two classification variation patterns in the first feature classification result and the second feature classification result as the first feature type and the second feature type, as shown in fig. 8, the feature type with the largest pixel area of the two classification variation patterns in the first feature classification result and the second feature classification result is the farmland and the water area respectively, therefore, it is determined that the first type of land feature is a cultivated land type, the second type of land feature is a water area type, that is, the change pattern spot type of the two-classification change pattern spots is changed from cultivated land to water area, that is, cultivated land is lost, and a scene schematic diagram after the change is shown in fig. 9.

In an alternative embodiment, the first feature type and the second feature type are selected by the number of pixels corresponding to each of the plurality of feature types included in the variation pattern patch.

Optionally, the selecting the first ground feature type and the second ground feature type according to the number of pixels corresponding to each ground feature type in the plurality of ground feature types included in the variation pattern spot includes: and selecting the feature type with the largest number of pixels (namely the largest pixel area) of the changed pattern spots in the first feature classification result and the second feature classification result as the first feature type and the second feature type corresponding to the changed pattern spots. For example, the first feature classification result of a variation pattern comprises

A pixel therein

Each pixel is of a water area type,

each pixel is of a type of forest land,

the individual pixels are of the grass type, in which,

the first feature type corresponding to the variation pattern spot is

The corresponding ground feature type.

As an alternative embodiment, a graphical user interface is provided by an electronic device, where content displayed on the graphical user interface at least partially includes a feature classification display scene, and the image processing method further includes:

step S402, displaying the corresponding ground object type of the change pattern spot in the first image in a first display mode in the graphical user interface;

step S404, when determining the type of the variation pattern to which the variation pattern belongs, dynamically jumping from the first display mode to a second display mode to display the type of the feature corresponding to the variation pattern in the second image.

Optionally, the first display manner and the second display manner are respectively different graphs, line segments, colors, and the like, which are not specifically limited in the embodiment of the present application.

For example, in the graphical user interface shown in fig. 5a, the vertical broken lines are used to show the type of the farmland corresponding to the change pattern patch in the first image, and in the graphical user interface shown in fig. 5b, the horizontal broken lines are used to show the type of the water area corresponding to the change pattern patch in the second image.

As another alternative embodiment, a graphical user interface is provided by an electronic device, where content displayed on the graphical user interface at least partially includes a feature classification display scene, and the image processing method further includes:

step S502, dynamically displaying the first ground feature classification result in a first display area of the graphical user interface, and dynamically displaying the second ground feature classification result in a second display area of the graphical user interface;

step S504 is to receive a confirmation command of the first feature classification result and the second feature classification result, determine to perform change detection processing on the first image and the second image to obtain a change pattern patch, and determine the type of the change pattern patch based on the first feature classification result and the second feature classification result.

In the above optional embodiment, in the graphical user interface, the user may confirm the first feature classification result and the second feature classification result that are currently displayed, that is, the user may touch a confirmation button in the graphical user interface, or click the first feature classification result and the second feature classification result, trigger a confirmation instruction, to further determine to perform change detection processing on the first image and the second image to obtain a change pattern spot, and determine the type of the change pattern spot based on the first feature classification result and the second feature classification result.

step S602, dynamically displaying the first feature classification result in a first display area of the gui, and dynamically displaying the second feature classification result in a second display area of the gui;

step S604, receiving a modification command of the first feature classification result and/or the second feature classification result, determining to perform change detection processing on the first image and the second image to obtain a change pattern, and determining the type of the change pattern based on the modified first feature classification result and/or the modified second feature classification result.

In the above optional embodiment, in the graphical user interface, the user may modify the currently displayed first feature classification result and second feature classification result, that is, the user touches a modification button or a change button in the graphical user interface, or double-clicks the first feature classification result and the second feature classification result, triggers a modification instruction, to further determine to perform change detection processing on the first image and the second image to obtain a change pattern, and determines the type of the change pattern based on the modified first feature classification result and/or the modified second feature classification result.

The present application provides another image processing method as shown in fig. 10. Fig. 10 is a flowchart of another image processing method according to an embodiment of the invention, as shown in fig. 10, the method includes the following steps:

step S702, acquiring a first satellite image and a second satellite image of a coastal local agricultural area;

step S704, performing feature classification processing on the first satellite image and the second satellite image respectively to obtain a first feature classification result and a second feature classification result, wherein different colors are respectively adopted in the first feature classification result and the second feature classification result to represent corresponding feature types;

step S706, change detection processing is carried out on the first satellite image and the second satellite image, and a change pattern spot is obtained;

step S708, determining a type of the change pattern spot in the first satellite image as a cultivated land type based on the first feature classification result, and determining a type of the change pattern spot in the second satellite image as a greenhouse type based on the second feature classification result;

step S710, determining the change type of the change pattern spot to be changed from the cultivated land type to the greenhouse type.

As an optional embodiment, the image processing method provided by the present application can be applied to the fields of agriculture and forestry (for example, crop extraction (wheat, rice, potato, etc.), unmanned aerial vehicle crop identification (corn, flue-cured tobacco, myotonin, etc.), land parcel identification, growth monitoring (index calculation), agricultural estimation, pest and disease damage monitoring, planting suggestion pushing, etc.), secondary disaster fields (for example, disaster monitoring, disaster early warning, etc.), and in particular, can be applied to the field of farmland monitoring in coastal local agricultural areas.

Optionally, the first satellite image is an image obtained by shooting the local agricultural area on the coast at a first time, the second satellite image is an image obtained by shooting the local agricultural area on the coast at a second time, and the second time is later than the first time. As an alternative embodiment, the first satellite image and the second satellite image may be replaced by aerial images.

Optionally, in this embodiment of the application, the type of the change pattern spot to which the change pattern spot belongs is determined based on the first land feature classification result and the second land feature classification result, for example, the first land feature classification result is an arable land type, the second land feature classification result is a water area type, and since the land feature classification result in the coastal local agricultural area is changed from the arable land type to a greenhouse type, but the greenhouse is still built on the basis of the existence of arable land and does not exist instead of arable land, but only a mode of existence of arable land is changed, that is, the change pattern spot is not lost by arable land.

According to the embodiment of the invention, the attributes of the front-stage and rear-stage change patterns extracted by the two-classification change detection are assigned by utilizing the ground feature classification information, and the change pattern detection and the pattern classification are decoupled, so that the positions of the change patterns can be extracted, the corresponding pattern types can be flexibly obtained according to actual requirements, the technical effects of flexibly and quickly realizing the function of multi-classification change detection without additional data marking and model development can be realized, and the technical problem of inaccurate classification results caused by the adoption of a classification change detection method for carrying out ground feature classification processing on remote sensing images in the prior art is solved.

As an alternative embodiment, the obtaining the first ground object classification result and the second ground object classification result by performing ground object classification processing on the first satellite image and the second satellite image respectively includes:

step S802, classifying each pixel unit in the first satellite image according to the feature type corresponding to each pixel unit in the first satellite image to obtain a first feature classification result;

step S804, performing classification processing on each pixel unit in the second satellite image according to the feature type corresponding to each pixel unit in the second satellite image, so as to obtain the second feature classification result.

In an embodiment of the present invention, a feature classification process is performed on the first satellite image, that is, each pixel unit in the first satellite image is classified to obtain the first feature classification result, where the first feature classification result is used to determine a change patch type to which the change patch belongs; and performing feature classification processing on the second satellite image, namely performing classification processing on each pixel unit in the second satellite image to obtain a second feature classification result, wherein the second feature classification result is used for determining the type of the change pattern to which the change pattern belongs.

The present application provides another image processing method as shown in fig. 11. Fig. 11 is a flowchart of another image processing method according to an embodiment of the invention, as shown in fig. 11, the method includes the following steps:

step S902, a first satellite image and a second satellite image in a local construction area of a city are obtained;

step S904, performing feature classification processing on the first satellite image and the second satellite image respectively to obtain a first feature classification result and a second feature classification result, wherein different colors are respectively adopted in the first feature classification result and the second feature classification result to represent corresponding feature types;

step S906, change detection processing is carried out on the first satellite image and the second satellite image, and a change pattern spot is obtained;

step S908, determining a type of the change pattern spot in the first satellite image as a building type based on the first feature classification result, and determining a type of the change pattern spot in the second satellite image as a park type based on the second feature classification result;

step S910, determining the change type of the change pattern spot to change from the building type to the park type.

As an optional embodiment, the image processing method provided in the present application may be applied to the field of urban planning (e.g., road network extraction (satellite, unmanned aerial vehicle), building extraction, building change detection (satellite, unmanned aerial vehicle), fire protection, etc.), and for example, may be used to monitor whether an urban construction land in an urban local construction area is allocated to a park construction land.

Optionally, the first satellite image is an image obtained by shooting the local urban construction area at a first time, the second satellite image is an image obtained by shooting the local urban construction area at a second time, and the second time is later than the first time. As an alternative embodiment, the first satellite image and the second satellite image may be replaced by aerial images.

Optionally, in this embodiment of the application, a change pattern spot type to which the change pattern spot belongs is determined based on the first feature classification result and the second feature classification result, for example, a change pattern spot type of the change pattern spot in the first satellite image is determined as a building type based on the first feature classification result, and a change pattern spot type of the change pattern spot in the second satellite image is determined as a park type based on the second feature classification result; and further determining that the ground feature classification result in the local construction area of the city is changed from the building type to the park type.

The present application provides another image processing method as shown in fig. 12. Fig. 12 is a flowchart of another image processing method according to an embodiment of the invention, as shown in fig. 12, the method includes the following steps:

step S1002, acquiring a first satellite image and a second satellite image in a local drainage basin area of a river channel;

step S1004, performing feature classification processing on the first satellite image and the second satellite image respectively to obtain a first feature classification result and a second feature classification result, wherein different colors are respectively adopted in the first feature classification result and the second feature classification result to represent corresponding feature types;

step S1006, change detection processing is carried out on the first satellite image and the second satellite image, and a change pattern spot is obtained;

step S1008, determining a change pattern spot type of the change pattern spot in the first satellite image as a river type based on the first feature classification result, and determining a change pattern spot type of the change pattern spot in the second satellite image as a scenic spot type based on the second feature classification result;

step S1010, determining a change type of the change pattern spot to be changed from the river type to the scenic spot type.

As an alternative embodiment, the image processing method provided in the present application can be applied to the fields of natural resources and ecological environment (e.g., weather forecast, change detection, ecological red line change detection, multi-classification change detection, ground feature classification, greenhouse extraction, road network extraction, building change detection (satellite, unmanned aerial vehicle), etc.), the fields of water conservancy (e.g., water area change detection, greenhouse extraction, water body extraction (optical, radar), sheet forest extraction, net cage culture extraction, sand pit extraction, river house extraction, barrage extraction, photovoltaic power plant extraction, etc.), for example, it can be specifically applied to monitoring whether the river type is changed to the scenic region type in the local river basin region of the river channel.

Optionally, in this embodiment of the application, a change pattern spot type to which the change pattern spot belongs is determined based on the first feature classification result and the second feature classification result, for example, a change pattern spot type of the change pattern spot in the first satellite image is determined as a river type based on the first feature classification result, and a change pattern spot type of the change pattern spot in the second satellite image is determined as a scenic spot type based on the second feature classification result; and further determining that the ground feature classification result in the local construction area of the city is converted from river type to scenic spot type.

The present application provides yet another image processing method as shown in fig. 13. Fig. 13 is a schematic diagram illustrating image processing performed by the cloud server according to an embodiment of the present invention, and as shown in fig. 13, the client uploads the first image and the second image within the target area to the cloud server. The cloud server performs feature classification processing on the first image and the second image respectively to obtain a first feature classification result and a second feature classification result, performs change detection processing on the first image and the second image to obtain a change pattern patch, and determines a change pattern patch type to which the change pattern patch belongs based on the first feature classification result and the second feature classification result. Then, the cloud server feeds back the change spot type to the client. The final changed blob type is presented to the user through the graphical user interface of the client. The alternative ways of displaying the changed blob types on the graphical user interface have been described in the above embodiments, and are not described here.

The image processing method provided in the embodiment of the present application may be applied to, but not limited to, a remote sensing intelligent analysis scene, and performs, by means of an interaction between the SaaS server and the client, a ground feature classification process and a change detection process based on the obtained remote sensing image in the target area range, so as to determine a change pattern type to which a change pattern belongs in the remote sensing image.

Optionally, the client is a remote sensing intelligent analysis device, the remote sensing intelligent analysis device monitors a first image and a second image in a target area range, and sends the first image and the second image to the server, the server performs feature classification processing on the first image and the second image respectively to obtain a first feature classification result and a second feature classification result, performs change detection processing on the first image and the second image to obtain a change pattern spot, and determines a change pattern spot type to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

It should be noted that for simplicity of explanation, the foregoing method embodiments are described as a series of acts or combination of acts, but those skilled in the art will appreciate that the present invention is not limited by the order of acts or acts described, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

Example 2

According to an embodiment of the present invention, there is further provided an apparatus for implementing the image processing method, and fig. 14 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present invention, as shown in fig. 14, the image processing apparatus includes: an obtaining module 40, a first processing module 42, a second processing module 44, and a determining module 46, wherein:

the acquiring module 40 is configured to acquire a first image and a second image within a target area; the first processing module 42 is configured to perform feature classification processing on the first image and the second image respectively to obtain a first feature classification result and a second feature classification result; the second processing module 44 is configured to perform change detection processing on the first image and the second image to obtain a change pattern; the determining module 46 is configured to determine a type of the variation pattern attributed to the variation pattern based on the first feature classification result and the second feature classification result.

It should be noted that the above modules may be implemented by software or hardware, for example, for the latter, the following may be implemented: the modules can be located in the same processor; alternatively, the modules may be located in different processors in any combination.

It should be noted here that the acquiring module 40, the first processing module 42, the second processing module 44, and the determining module 46 correspond to steps S202 to S208 in embodiment 1, and the modules are the same as the corresponding steps in implementation examples and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above may be implemented in a computer terminal as part of an apparatus.

It should be noted that, reference may be made to the relevant description in embodiment 1 for alternative or preferred embodiments of this embodiment, and details are not described here again.

Example 3

According to an embodiment of the present invention, there is also provided an image processing system, including:

a processor; and a memory, connected to the processor, for providing instructions to the processor for processing the following processing steps: acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

It should be noted that, for a preferred implementation of this embodiment, reference may be made to the relevant description in embodiment 1, and details are not repeated here, and any one of the optional or preferred image processing methods in embodiment 1 may be executed or implemented in the image processing system provided in this embodiment.

Example 4

According to an embodiment of the present invention, an embodiment of a computer terminal is provided, where the computer terminal may be any one computer terminal device in a computer terminal group. Optionally, in the embodiment of the present invention, the computer terminal may also be replaced with a terminal device such as a mobile terminal.

Optionally, in the embodiment of the present invention, the computer terminal may be located in at least one network device of a plurality of network devices of a computer network.

In this embodiment of the present invention, the computer terminal may execute the program code of the following steps in the vulnerability detection method of the application program: acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

Alternatively, fig. 15 is a block diagram of a computer terminal according to an embodiment of the present invention. As shown in fig. 15, the computer terminal may include: one or more processors 602 (only one of which is shown), a memory 604, and a program stored on the memory and executable on the processor, and may further include a peripheral interface 606, wherein the memory 604 is connected to the processor 602 for providing the processor with instructions to process the following processing steps: acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the security vulnerability detection method and apparatus in the embodiments of the present invention, and the processor executes various functional applications and data processing by operating the software programs and modules stored in the memory, that is, the above-mentioned method for detecting a system vulnerability attack is implemented. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring a first image and a second image within a target area range, wherein the first image is an image obtained by shooting the target area range at a first time, the second image is an image obtained by shooting the target area range at a second time, and the second time is later than the first time; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

Optionally, the processor may further execute the program code of the following steps: and classifying each pixel unit in the first image according to the feature type corresponding to each pixel unit in the first image to obtain the first feature classification result.

Optionally, the processor may further execute the program code of the following steps: and classifying each pixel unit in the second image according to the feature type corresponding to each pixel unit in the second image to obtain the second feature classification result.

Optionally, the processor may further execute the program code of the following steps: and performing classification change detection processing on the first image and the second image to extract a classification change pattern spot.

Optionally, the processor may further execute the program code of the following steps: determining a first feature type corresponding to the change pattern spot based on the first feature classification result; determining a second ground object type corresponding to the change pattern spot based on the second ground object classification result; determining the type change of the ground feature by using the first ground feature type and the second ground feature type; and determining the type of the change pattern spot to which the change pattern spot belongs according to the type change of the ground object.

Optionally, the processor may further execute the program code of the following steps: displaying the ground object type corresponding to the change pattern spots in the first image in a first display mode in a graphical user interface; and when determining the type of the change pattern spot to which the change pattern spot belongs, dynamically jumping from the first display mode to a second display mode to display the type of the ground object corresponding to the change pattern spot in the second image.

Optionally, the processor may further execute the program code of the following steps: dynamically displaying the first ground object classification result in a first display area of a graphical user interface, and dynamically displaying the second ground object classification result in a second display area of the graphical user interface; receiving a confirmation command of the first feature classification result and the second feature classification result, determining to perform change detection processing on the first image and the second image to obtain a change pattern spot, and determining the type of the change pattern spot based on the first feature classification result and the second feature classification result.

Optionally, the processor may further execute the program code of the following steps: dynamically displaying the first ground object classification result in a first display area of a graphical user interface, and dynamically displaying the second ground object classification result in a second display area of the graphical user interface; receiving a modification instruction of the first feature classification result and/or the second feature classification result, determining to perform change detection processing on the first image and the second image to obtain a change pattern patch, and determining the type of the change pattern patch based on the modified first feature classification result and/or the modified second feature classification result.

The embodiment of the invention provides an image processing scheme. Acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

Therefore, the method and the device achieve the purpose of obtaining the multi-classification change image spots in a mode of combining classification change detection and surface feature classification processing, and further solve the technical problem that in the prior art, the classification result is inaccurate when the remote sensing image is subjected to surface feature classification processing by adopting a classification change detection method.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring a first satellite image and a second satellite image of a coastal local agricultural area; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining the type of the change pattern spot in the first satellite image as the cultivated land type based on the first ground feature classification result, and determining the type of the change pattern spot in the second satellite image as the greenhouse type based on the second ground feature classification result; determining the change type of the change pattern spot to be changed from the cultivated land type to the greenhouse type.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring a first satellite image and a second satellite image in a local construction area of a city; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining a change pattern spot type of the change pattern spot in the first satellite image as a building type based on the first feature classification result, and determining a change pattern spot type of the change pattern spot in the second satellite image as a park type based on the second feature classification result; determining a change type of the change pattern spot to be changed from the building type to the park type.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring a first satellite image and a second satellite image in a local drainage basin region of a river channel; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining a change pattern spot type of the change pattern spot in the first satellite image as a river type based on the first feature classification result, and determining a change pattern spot type of the change pattern spot in the second satellite image as a scenic spot type based on the second feature classification result; determining a change type of the change pattern spot to be changed from the river type to the scenic spot type.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: receiving a first image and a second image within a target area range from a client; respectively carrying out feature classification processing on the first image and the second image to obtain a first feature classification result and a second feature classification result, carrying out change detection processing on the first image and the second image to obtain a change pattern patch, and determining a change pattern patch type to which the change pattern patch belongs based on the first feature classification result and the second feature classification result; and feeding back the type of the change pattern spot to the client.

It can be understood by those skilled in the art that the structure shown in fig. 15 is only an illustration, and the computer terminal may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 15 is a diagram illustrating a structure of the electronic device. For example, the computer terminal may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 15, or have a different configuration than shown in FIG. 15.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the computer-readable storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

Example 5

Embodiments of a computer-readable storage medium are also provided according to embodiments of the present invention. Optionally, in this embodiment, the computer-readable storage medium may be configured to store the program code executed by the image processing method provided in embodiment 1.

Optionally, in this embodiment of the present invention, the computer-readable storage medium may be located in any one computer terminal in a computer terminal group in a computer network, or in any one mobile terminal in a mobile terminal group.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: acquiring a first image and a second image within a target area range; respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result; carrying out change detection processing on the first image and the second image to obtain a change pattern spot; and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: and classifying each pixel unit in the first image according to the feature type corresponding to each pixel unit in the first image to obtain the first feature classification result.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: and classifying each pixel unit in the second image according to the feature type corresponding to each pixel unit in the second image to obtain the second feature classification result.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: and performing classification change detection processing on the first image and the second image to extract a classification change pattern spot.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: determining a first feature type corresponding to the change pattern spot based on the first feature classification result; determining a second ground object type corresponding to the change pattern spot based on the second ground object classification result; determining the type change of the ground feature by using the first ground feature type and the second ground feature type; and determining the type of the change pattern spot to which the change pattern spot belongs according to the type change of the ground object.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: displaying the ground object type corresponding to the change pattern spots in the first image in a first display mode in a graphical user interface; and when determining the type of the change pattern spot to which the change pattern spot belongs, dynamically jumping from the first display mode to a second display mode to display the type of the ground object corresponding to the change pattern spot in the second image.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: dynamically displaying the first ground object classification result in a first display area of a graphical user interface, and dynamically displaying the second ground object classification result in a second display area of the graphical user interface; receiving a confirmation command of the first feature classification result and the second feature classification result, determining to perform change detection processing on the first image and the second image to obtain a change pattern spot, and determining the type of the change pattern spot based on the first feature classification result and the second feature classification result.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: dynamically displaying the first ground object classification result in a first display area of a graphical user interface, and dynamically displaying the second ground object classification result in a second display area of the graphical user interface; receiving a modification instruction of the first feature classification result and/or the second feature classification result, determining to perform change detection processing on the first image and the second image to obtain a change pattern patch, and determining the type of the change pattern patch based on the modified first feature classification result and/or the modified second feature classification result.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: acquiring a first satellite image and a second satellite image of a coastal local agricultural area; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining the type of the change pattern spot in the first satellite image as the cultivated land type based on the first ground feature classification result, and determining the type of the change pattern spot in the second satellite image as the greenhouse type based on the second ground feature classification result; determining the change type of the change pattern spot to be changed from the cultivated land type to the greenhouse type.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: acquiring a first satellite image and a second satellite image in a local construction area of a city; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining a change pattern spot type of the change pattern spot in the first satellite image as a building type based on the first feature classification result, and determining a change pattern spot type of the change pattern spot in the second satellite image as a park type based on the second feature classification result; determining a change type of the change pattern spot to be changed from the building type to the park type.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: acquiring a first satellite image and a second satellite image in a local drainage basin region of a river channel; respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types; carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot; determining a change pattern spot type of the change pattern spot in the first satellite image as a river type based on the first feature classification result, and determining a change pattern spot type of the change pattern spot in the second satellite image as a scenic spot type based on the second feature classification result; determining a change type of the change pattern spot to be changed from the river type to the scenic spot type.

Optionally, in an embodiment of the present invention, the computer readable storage medium is configured to store program codes for performing the following steps: receiving a first image and a second image within a target area range from a client; respectively carrying out feature classification processing on the first image and the second image to obtain a first feature classification result and a second feature classification result, carrying out change detection processing on the first image and the second image to obtain a change pattern patch, and determining a change pattern patch type to which the change pattern patch belongs based on the first feature classification result and the second feature classification result; and feeding back the type of the change pattern spot to the client.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a computer-readable storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. An image processing method, comprising:

acquiring a first image and a second image within a target area range;

respectively carrying out ground object classification processing on the first image and the second image to obtain a first ground object classification result and a second ground object classification result;

carrying out change detection processing on the first image and the second image to obtain a change pattern spot;

and determining the type of the change pattern spot to which the change pattern spot belongs based on the first feature classification result and the second feature classification result.

2. The image processing method of claim 1, wherein the performing feature classification processing on the first image and the second image to obtain the first feature classification result and the second feature classification result respectively comprises:

classifying each pixel unit in the first image according to the feature type corresponding to each pixel unit in the first image to obtain a first feature classification result;

and classifying each pixel unit in the second image according to the feature type corresponding to each pixel unit in the second image to obtain a second feature classification result.

3. The image processing method according to claim 1, wherein performing change detection processing on the first image and the second image to obtain the change patches comprises:

and carrying out classification change detection processing on the first image and the second image, and extracting classification change pattern spots.

4. The image processing method according to claim 1, wherein determining the change patch type to which the change patch belongs based on the first feature classification result and the second feature classification result comprises:

determining a first feature type corresponding to the change pattern spot based on the first feature classification result;

determining a second ground object type corresponding to the change pattern spots based on the second ground object classification result;

determining a feature type change by using the first feature type and the second feature type;

and determining the type of the change pattern spot to which the change pattern spot belongs according to the type change of the ground object.

5. The image processing method as claimed in claim 1, wherein a graphical user interface is provided by the electronic device, the content displayed by the graphical user interface at least partially includes a feature classification display scene, and the image processing method further comprises:

displaying the ground feature type corresponding to the change pattern spot in the first image in a first display mode in the graphical user interface;

and when determining the type of the changed pattern spot to which the changed pattern spot belongs, dynamically jumping from the first display mode to a second display mode to display the type of the ground object corresponding to the changed pattern spot in the second image.

6. The image processing method as claimed in claim 1, wherein a graphical user interface is provided by the electronic device, the content displayed by the graphical user interface at least partially includes a feature classification display scene, and the image processing method further comprises:

dynamically displaying the first terrain classification result in a first display area of the graphical user interface and dynamically displaying the second terrain classification result in a second display area of the graphical user interface;

receiving a confirmation instruction of the first feature classification result and the second feature classification result, determining to perform change detection processing on the first image and the second image to obtain a change pattern spot, and determining the type of the change pattern spot based on the first feature classification result and the second feature classification result.

7. The image processing method as claimed in claim 1, wherein a graphical user interface is provided by the electronic device, the content displayed by the graphical user interface at least partially includes a feature classification display scene, and the image processing method further comprises:

receiving a modification instruction of the first feature classification result and/or the second feature classification result, determining to perform change detection processing on the first image and the second image to obtain a change pattern patch, and determining the type of the change pattern patch based on the modified first feature classification result and/or the modified second feature classification result.

8. An image processing method, comprising:

acquiring a first satellite image and a second satellite image of a coastal local agricultural area;

respectively carrying out ground feature classification processing on the first satellite image and the second satellite image to obtain a first ground feature classification result and a second ground feature classification result, wherein different colors are respectively adopted in the first ground feature classification result and the second ground feature classification result to represent corresponding ground feature types;

carrying out change detection processing on the first satellite image and the second satellite image to obtain a change pattern spot;

determining the type of the change pattern spot in the first satellite image as the cultivated land type based on the first ground feature classification result, and determining the type of the change pattern spot in the second satellite image as the greenhouse type based on the second ground feature classification result;

determining that the change type of the change pattern spot is changed from the farmland type to the greenhouse type.

9. The image processing method of claim 8, wherein the performing feature classification processing on the first satellite image and the second satellite image respectively to obtain the first feature classification result and the second feature classification result comprises:

classifying each pixel unit in the first satellite image according to the feature type corresponding to each pixel unit in the first satellite image to obtain a first feature classification result;

and classifying each pixel unit in the second satellite image according to the ground feature type corresponding to each pixel unit in the second satellite image to obtain a second ground feature classification result.

10. An image processing method, comprising:

receiving a first image and a second image within a target area range from a client;

respectively carrying out feature classification processing on the first image and the second image to obtain a first feature classification result and a second feature classification result, carrying out change detection processing on the first image and the second image to obtain a change pattern patch, and determining the type of the change pattern patch to which the change pattern patch belongs based on the first feature classification result and the second feature classification result;

and feeding back the type of the changed pattern spot to the client.

11. A computer-readable storage medium, comprising a stored program, wherein when the program runs, the computer-readable storage medium controls an apparatus to execute the image processing method according to any one of claims 1 to 10.

12. An image processing system, comprising:

a processor; and

a memory coupled to the processor for providing instructions to the processor for processing the following processing steps:

acquiring a first image and a second image within a target area range, wherein the first image is an image obtained by shooting the target area range at a first moment, the second image is an image obtained by shooting the target area range at a second moment, and the second moment is later than the first moment;