CN114943931A - High-resolution image water surface rate analysis method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a high-resolution image water surface rate analysis method, a high-resolution image water surface rate analysis device, electronic equipment and a storage medium, wherein the high-resolution image water surface rate analysis method comprises the following steps: acquiring a high-resolution remote sensing image of a target monitoring area, and preprocessing the high-resolution remote sensing image to obtain reflectivity data of the high-resolution remote sensing image; extracting the high-resolution image through the reflectivity data to obtain a water body coarse result; extracting a target monitoring area through a data set, and determining an image shadow according to a water body coarse result and an extraction result; determining a water body fine result according to the water body coarse result and the image shadow; and determining the image water surface rate of the target monitoring area according to the water body fine result. The invention simplifies the distinguishing process of water and shadow noise by fully mining the advantages of UWI index and FROM-GLC10 data, thereby ensuring the completeness of water extraction and reducing shadow confusion, greatly reducing the phenomena of water area missing extraction and other ground object wrong extraction and the like, reducing the manual workload, improving the extraction efficiency and ensuring the accuracy of the water surface rate result.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a high-resolution image water surface rate analysis method and device, electronic equipment and a storage medium.
Background
The water surface rate is the ratio of the water area bearing the water area function to the total area of the area, is a controllability index of city planning and water system management, and is an intuitive expression form of the water area size. Whether the water surface rate is reasonable or not influences the normal exertion of the urban ecological environment function and is related to the livable degree of the settlement environment. The water area types related to the water surface rate mainly comprise natural or artificial land surface fresh water bodies such as rivers, lakes, reservoirs, wetlands, swamps, ponds, pits and the like. With the vigorous development of the remote sensing technology, the water body can be extracted in a large area based on the remote sensing image, and a new technical means is provided for the rapid calculation of the water surface rate.
At present, the method for extracting the water body based on the remote sensing image mainly comprises the following steps: a threshold method, a water body index method, an object-oriented method, a deep learning model, and the like. The threshold method is used for identifying based on the fact that the reflectivity of the water body in the single infrared band is low, the method is simple and convenient, but the water body in a small area cannot be extracted, and the threshold value can be determined only by multiple attempts; the water body index method is based on water body spectral characteristics, constructs mathematical formulas such as difference values or ratios and the like to highlight water body information, inhibits other background ground objects, and extracts the water body by simple band operation; the object-oriented method is based on a segmentation idea, images are divided into homogeneous objects with different sizes, and water bodies are distinguished by combining information such as spectrum and texture; deep learning is a relatively hot method in recent years, water body extraction is realized by building different neural network models and training samples, but sample drawing and model construction are time-consuming and labor-consuming, and uncertain factors also exist in parameter adjustment. In addition, because the shadow generated by the shielding of buildings, mountains and the like is close to the spectral information of the water body, the water body is easy to be mixed with the shadow to generate a large amount of error pattern spots when the method is used for extracting the water body.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a high-resolution image water surface rate analysis method and device, electronic equipment and a storage medium, which simplify the process, reduce the manual workload, improve the extraction efficiency and ensure the accuracy of a water surface rate result.
One aspect of the present invention provides a method for analyzing a water surface rate of a high-resolution image, including:
responding to an analysis request, acquiring a high-resolution remote sensing image of a target monitoring area, and performing pretreatment on the high-resolution remote sensing image to obtain reflectivity data of the high-resolution remote sensing image, wherein the pretreatment corrects the high-resolution remote sensing image through preset parameters;
extracting the high-resolution image through the reflectivity data to obtain a first water body distribution result;
extracting the target monitoring area through a data set to obtain an extraction result based on the data set standard, and determining an image shadow according to the first water body distribution result and the extraction result;
determining a second water body distribution result according to the first water body distribution result and the image shadow;
and determining the image water surface rate of the target monitoring area according to the second water body distribution result.
According to the analysis method for the water surface rate of the high-resolution remote sensing image, the step of preprocessing the high-resolution remote sensing image to obtain the reflectivity data of the high-resolution remote sensing image comprises the following steps:
carrying out radiation correction on the high-resolution remote sensing image through a calibration coefficient to generate radiance data;
and performing atmospheric correction on the radiance data to obtain the reflectivity data.
According to the high-resolution image water surface rate analysis method, the method further comprises the following steps:
performing orthorectification on the reflectivity data through RPC parameters and DEM data of the multispectral image and the panchromatic image;
performing image fusion on the multispectral image and the panchromatic image which are subjected to orthorectification;
and carrying out mosaic splicing and color homogenizing on the remote sensing image, and cutting according to preset partitions.
According to the high-resolution image water surface rate analysis method, extracting the high-resolution image through the reflectivity data to obtain a first water body distribution result comprises the following steps:
calculating the UWI urban water body index by using a UWI calculation formula
G, R, NIR is a green band, a red band and a near-red band of the remote sensing image respectively;
and carrying out binarization on the image of the UWI urban water body index according to a set threshold value, using the image exceeding the set threshold value as a water body, and carrying out first assignment processing on the water body and a non-water body area to obtain a first water body distribution result, wherein the first assignment processing comprises respectively assigning values to the water body and the non-water body.
According to the high-resolution image water surface rate analysis method, extracting the target monitoring area through a data set to obtain an extraction result based on the data set standard, and determining an image shadow according to the first water body distribution result and the extraction result, the method comprises the following steps:
cutting FROM-GLC10 data including the target monitoring area;
re-classifying the FROM-GLC10 data, re-classifying the result through 3 × 3 convolution kernel corrosion, and further re-sampling the re-classified result to obtain a re-sampled image;
and intersecting the resampled image with the first water body distribution result to generate an initial shadow, corroding the initial shadow through a 3-by-3 convolution core, and eliminating small broken spots of the initial shadow by setting a small broken spot deleting threshold to obtain the fine image shadow.
According to the high-resolution image water surface rate analysis method, determining the image water surface rate of the target monitoring area according to the second water body distribution result comprises the following steps:
corroding the obtained first water body result to remove burrs, deleting small broken spots, further performing vectorization, overlapping the vectorization and the remote sensing image, deleting and modifying error vectors, and supplementing a missed-extraction area to obtain a second water body distribution result, wherein the second water body distribution result is used for representing an accurate water area range;
and calculating the accurate water area and the total area of the remote sensing image area, and determining the image water surface rate of the target monitoring area according to the ratio of the accurate water area to the total area of the remote sensing image area.
Another aspect of the embodiments of the present invention provides a high resolution image water surface rate analysis apparatus, including:
the preprocessing module is used for responding to an analysis request, obtaining a high-resolution remote sensing image of a target monitoring area, and performing preprocessing on the high-resolution remote sensing image to obtain reflectivity data of the high-resolution remote sensing image, wherein the preprocessing corrects the high-resolution remote sensing image through preset parameters;
the extraction module is used for extracting the high-resolution images through the reflectivity data to obtain a first water body distribution result;
the image shadow module is used for extracting the target monitoring area through a data set to obtain an extraction result based on the data set standard, and determining an image shadow according to the first water body distribution result and the extraction result;
the accurate water area module is used for determining a second water body distribution result according to the first water body distribution result and the image shadow;
and the image water surface rate module is used for determining the image water surface rate of the target monitoring area according to the second water body distribution result.
Another aspect of the embodiments of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor, to cause the computer device to perform the methods described above.
The invention has the beneficial effects that: the high-resolution image water surface rate calculation method based on combination of the UWI index and FROM-GLC10 data fully exploits the advantages of the UWI index and FROM-GLC10 data, simplifies the distinguishing process of water body and shadow noise, ensures complete water body extraction, greatly reduces phenomena of shadow confusion, water area missing extraction, other ground object wrong extraction and the like, and can be applied to large-area engineering rapid statistics of the water surface rate of a certain area.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow diagram of a method of an embodiment of the invention.
FIG. 2 is a detailed flowchart of the method for calculating the water surface rate of high-resolution images based on the combination of UWI index and FROM-GLC10 data according to the embodiment of the invention.
FIG. 3 is a schematic diagram of the water body roughness results of an embodiment of the invention.
FIG. 4 is a schematic diagram of image shading according to an embodiment of the present invention.
FIG. 5 is a schematic diagram of a final water body result according to an embodiment of the present invention.
FIG. 6 is an enlarged view of the final water result of an embodiment of the present invention.
Fig. 7 is a diagram of an analysis apparatus for analyzing a water surface rate of a high-resolution image according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. In the following description, suffixes such as "module", "part", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no peculiar meaning in itself. Thus, "module", "component" or "unit" may be used mixedly. "first", "second", etc. are used for the purpose of distinguishing technical features only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the precedence of the indicated technical features. In the following description, the method steps are labeled continuously for convenience of examination and understanding, and the implementation sequence of the steps is adjusted without affecting the technical effect achieved by the technical scheme of the invention in combination with the overall technical scheme of the invention and the logical relationship among the steps. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, the embodiment of the invention provides a flow of a high-resolution image water surface rate analysis method, which includes, but is not limited to, steps S100 to S500.
And S100, responding to the analysis request, acquiring a high-resolution remote sensing image of the target monitoring area, and preprocessing the high-resolution remote sensing image to obtain reflectivity data of the high-resolution remote sensing image.
Step S200, extracting the high-resolution images through the reflectivity data to obtain a first water body distribution result, wherein the first water body distribution result is a water body coarse result.
And step S300, extracting the target monitoring area through the data set to obtain an extraction result based on the data set standard, and determining the image shadow according to the first water body distribution result and the extraction result.
And S400, determining a second water body distribution result according to the first water body distribution result and the image shadow.
And S500, determining the image water surface rate of the target monitoring area according to a second water body distribution result, wherein the second water body distribution result is the water body fine water area range.
As shown in fig. 2, the embodiment of the present invention provides a detailed flowchart of a high-resolution image water surface rate calculation method based on combination of the UWI index and FROM-GLC10 data, which sequentially includes the following steps:
step 1, acquiring a high-resolution remote sensing image of a monitoring area, and preprocessing the high-resolution remote sensing image into reflectivity data;
step 2, extracting a water body coarse result by using the reflectivity data;
step 3, acquiring FROM-GLC10 data of the monitoring area, and combining the water body rough result to obtain an image shadow;
step 4, subtracting the image shadow from the water body coarse result to extract a pure water body;
and 5, calculating the water surface rate of the image.
In some embodiments, step 1 specifically includes the following steps:
step 1.1, carrying out radiation correction on the original high-resolution image by using a calibration coefficient to generate radiance data;
step 1.2, performing atmospheric correction on the radiance data to generate reflectivity data;
step 1.3, performing orthorectification by using RPC parameters and DEM data carried by the multispectral image and the panchromatic image, correcting projection error and correcting geometric distortion;
step 1.4, performing image fusion on the multispectral image and the panchromatic image which are subjected to orthorectification, improving the resolution and increasing spectral information;
and step 1.5, carrying out mosaic splicing and color homogenizing on the remote sensing image, and cutting according to the administrative region.
In some embodiments, step 2 specifically includes the following steps:
step 2.1, calculating the UWI urban water body index, wherein the UWI calculation formula is as follows:
g, R, NIR represents the green band, the red band and the near-red band of the remote sensing image.
And 2.2, selecting a proper threshold value to carry out binarization on the UWI result image, wherein the value of the UWI result image is 1 when the UWI result image is larger than the threshold value, the value of the UWI result image is 0 when the UWI result image is smaller than the threshold value, and the coarse water result is obtained.
In some embodiments, step 3 specifically includes the following steps:
step 3.1, cutting FROM-GLC10 data containing a test area;
step 3.2, reclassifying the FROM-GLC10 data, assigning a water body as 0 and a non-water body as 1, selecting 3 x 3 convolution kernel corrosion reclassification results, reducing the roughness, and resampling the reclassification results according to the resolution of the used high-resolution remote sensing image to ensure that the resolutions are consistent;
and 3.3, intersecting the resampled image with a water body coarse result to generate an initial shadow, selecting 3 x 3 convolution to check corrosion, enabling the boundary to contract inwards, removing burrs and particle noise, setting a small broken patch deleting threshold, eliminating a large number of isolated small broken patches and obtaining a fine shadow.
In some embodiments, step 5 specifically includes the following steps:
step 5.1, corroding the water body result obtained in the step 4 to remove burrs and delete small broken spots to enable the result to be more complete, vectorizing the water body result, overlapping the vectorized water body result with the original image, deleting and modifying error vectors, and supplementing a missing extraction part to obtain an accurate water area range;
step 5.2, calculating the area of the water area and the total area of the image area, and calculating the water surface rate according to the following formula:
the water surface rate is 100% of the precise water area/total area of the area.
In some embodiments, based on fig. 2 and its detailed flow of the method for calculating the water surface rate of high-resolution images based on the combination of the UWI index and the FROM-GLC10 data, the technical solution of the present invention provides the following embodiments, and the implementation flow thereof is consistent with embodiment 2:
step 1, obtaining a high-resolution 6PMS remote sensing image of a certain city, wherein remote sensing image data comprises a panchromatic image (the resolution is 2 meters) and a multispectral image (the resolution is 8 meters), time phases are 10 months, 3 days and 11 months, 5 days in 2020, the image quality is good, the image completely covers the certain city, and the cloud part is removed through preprocessing steps of radiation correction, atmospheric correction, orthorectification, image fusion, mosaic color homogenization and the like, and the reflectivity data with accurate geometric positioning is obtained by cutting according to a land domain boundary vector of the certain city.
Wherein, the radiation correction is carried out by utilizing a scaling coefficient provided by an image metadata file, the panchromatic image outputs the apparent reflectivity, and the multispectral image outputs the radiance; in the atmospheric correction, an FLAASH model is selected to process the multispectral, so that the radiation error generated by atmospheric interference is eliminated, and reflectivity data are generated; the orthorectification is to eliminate geometric errors by using an RPC parameter file of an image and DEM data to obtain correct geometric positioning; the image fusion selects a PANSHARP method to fuse a panchromatic image and a multispectral image into a scene image, so that the spatial resolution of the multispectral image is improved, and spectral characteristic information is reserved; the mosaic color homogenizing is to splice two images into one scene and to cut the scene according to the boundary of some city land area.
And 2, referring to the schematic diagram of the water body rough result in fig. 3, and extracting the water body rough result by using the reflectivity data processed in the step 1. Wherein the content of the first and second substances,
step 2.1, calculating the UWI urban water body index, wherein the UWI calculation formula is as follows:
g, R, NIR represents the green band, red band and near red band of the remote sensing image, i.e. the 2 nd, 3 rd and 4 th bands of the GF6 image. Calculating the UWI urban water body index according to the formula on the data preprocessed in the step 1;
and 2.2, selecting-0.2 as a binarization threshold value to ensure that the water body range is fully covered, wherein the value is larger than-0.2, the water body is represented, the value is 1, and the value is 0 when the value is smaller than-0.2, the non-water part is represented.
And 3, acquiring FROM-GLC10 data of the monitoring area, and combining the water body rough result to obtain an image shadow, wherein the image shadow refers to the attached figure 4.
The FROM-GLC10 data is based on a world first set of global surface coverage products with 10-meter resolution developed by Sentinel-2 data in 2017, a classification system mainly comprises cultivated land, forest, grassland, shrub, wetland, water, orchards, artificial earth surfaces, bare land, glaciers and permanent snow, and the overall accuracy is 72.76%.
And 3.1, selecting a fromglc10v01_22_112.GIF data set containing a certain city according to a product naming rule of FROM-GLC10 data, and cutting to obtain 2017-year land coverage basic data of the certain city.
And 3.2, reclassifying the land cover data cut in the step 3.1, assigning a non-water part to be 1 and a water body to be 0 according to classification standards, selecting a 3 x 3 convolution kernel corrosion reclassification result, reducing the roughness, resampling the reclassification result to be 2 meters, and ensuring that the data resolution is consistent with the GF6-PMS data resolution used in the step 1.
And 3.3, intersecting the resampling result obtained in the step 3.2 with the water body coarse result obtained in the step 2 to generate an initial shadow, selecting 3-by-3 convolution to check corrosion, enabling the boundary to contract inwards, removing burrs and particle noise, setting a small broken patch deleting threshold value, eliminating a large number of isolated small broken patches and obtaining a fine shadow.
And 4, subtracting the shadow of the image in the step 3 from the coarse water body result in the step 2 to obtain the pure water body.
And 5, calculating the water surface rate of the image. Wherein the content of the first and second substances,
step 5.1, selecting a convolution kernel of 3 x 3 from the water body result obtained in the step 4 to corrode and remove burrs, deleting small patch with the area smaller than 100 pixels to enable the result to be more complete, vectorizing the patch, overlapping the vectorized patch with the original image, deleting and modifying error vectors, and supplementing a missing extraction part to obtain an accurate water area range;
step 5.2, calculating the area of the water area and the total area of the image area, and calculating the water surface rate according to the following formula:
referring to the analysis result diagram of the total water area of fig. 5, it is calculated that the extracted water area of a certain city is about 319.86 square kilometers, the extracted land area of a certain city is about 1753.99 square kilometers (sea area is not included), so that the water area rate of a certain city is about 18.24%, and fig. 6 is a partially enlarged analysis result diagram of the water area.
As shown in fig. 7, an embodiment of the present invention further provides an apparatus for analyzing water surface rate of high-resolution images, which includes a preprocessing module 701, an extracting module 702, an image shading module 703, a precise water area module 704, and an image water surface rate module 705;
the preprocessing module is used for responding to an analysis request, acquiring a high-resolution remote sensing image of a target monitoring area, preprocessing the high-resolution remote sensing image to obtain reflectivity data of the high-resolution remote sensing image, and correcting the high-resolution remote sensing image through preset parameters in the preprocessing; the extraction module is used for extracting the high-resolution images through the reflectivity data to obtain a first water body distribution result; the image shadow module is used for extracting the target monitoring area through the data set to obtain an extraction result based on the data set standard, and determining an image shadow according to the first water body distribution result and the extraction result; the accurate water area module is used for determining a second water body distribution result according to the first water body distribution result and the image shadow; and the image water surface rate module is used for determining the image water surface rate of the target monitoring area according to the second water body distribution result.
Exemplarily, under the cooperation of the preprocessing module, the extraction module, the image shadow module, the accurate water area module and the image water surface rate module in the device, the device of the embodiment can realize any one of the above-mentioned methods for analyzing the water surface rate of the high-resolution image, that is, obtaining the high-resolution remote sensing image of the target monitoring area in response to the analysis request, preprocessing the high-resolution remote sensing image to obtain the reflectivity data of the high-resolution remote sensing image, and correcting the high-resolution remote sensing image through the preset parameters; extracting the high-resolution image through the reflectivity data to obtain a first water body distribution result; extracting the target monitoring area through the data set to obtain an extraction result based on the data set standard, and determining an image shadow according to the first water body distribution result and the extraction result; determining a second water body distribution result according to the first water body distribution result and the image shadow; and determining the image water surface rate of the target monitoring area according to the second water body distribution result. The invention fully excavates the advantages of UWI index and FROM-GLC10 data, simplifies the distinguishing process of water body and shadow noise, not only ensures the completeness of water body extraction, but also reduces shadow confusion, greatly reduces the phenomena of water body extraction omission and other ground object false extraction, and the like, and can be applied to engineering large-area rapid statistics of the water surface rate of a certain area.
The embodiment of the invention also provides the electronic equipment, which comprises a processor and a memory;
the memory stores a program;
the processor executes a program to execute the high-resolution image water surface rate analysis method; the electronic device has a function of loading and operating a software system for analyzing the water level of the high-resolution image provided by the embodiment of the invention, such as a Personal Computer (PC), a mobile phone, a smart phone, a Personal Digital Assistant (PDA), a wearable device, a pocket PC (ppc), a tablet PC, and the like.
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method for analyzing the water surface rate of high-resolution images.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of larger operations are performed independently.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions can be read by a processor of the computer device from a computer readable storage medium, and the computer instructions can be executed by the processor, so that the computer device executes the high-resolution image water surface rate analysis method.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise indicated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units 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 or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a 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 storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A high resolution image water surface rate analysis method is characterized by comprising the following steps:
responding to an analysis request, acquiring a high-resolution remote sensing image of a target monitoring area, and performing pretreatment on the high-resolution remote sensing image to obtain reflectivity data of the high-resolution remote sensing image, wherein the pretreatment corrects the high-resolution remote sensing image through preset parameters;
extracting the high-resolution image through the reflectivity data to obtain a first water body distribution result;
extracting the target monitoring area through a data set to obtain an extraction result based on the data set standard, and determining an image shadow according to the first water body distribution result and the extraction result;
determining a second water body distribution result according to the first water body distribution result and the image shadow;
and determining the image water surface rate of the target monitoring area according to the second water body distribution result.
2. The method for analyzing the water surface rate of the high-resolution image according to claim 1, wherein the step of preprocessing the high-resolution remote sensing image to obtain the reflectivity data of the high-resolution remote sensing image comprises the following steps:
carrying out radiation correction on the high-resolution remote sensing image through a calibration coefficient to generate radiance data;
and performing atmospheric correction on the radiance data to obtain the reflectivity data.
3. The method for analyzing the water surface rate of high-resolution images according to claim 2, further comprising:
performing orthorectification on the reflectivity data through RPC parameters of the multispectral image and the panchromatic image and DEM data;
performing image fusion on the multispectral image and the panchromatic image which are subjected to orthorectification;
and carrying out mosaic splicing and color homogenizing on the remote sensing image, and cutting according to preset partitions.
4. The method for analyzing the water surface rate of the high-resolution image according to claim 1, wherein the extracting the high-resolution image according to the reflectivity data to obtain a first water distribution result comprises:
calculating the UWI urban water body index by using a UWI calculation formula
G, R, NIR is a green band, a red band and a near-red band of the remote sensing image respectively;
and carrying out binarization on the image of the UWI urban water body index according to a set threshold value, using the image exceeding the set threshold value as a water body, and carrying out first assignment processing on the water body and a non-water body area to obtain a first water body distribution result, wherein the first assignment processing comprises respectively assigning values to the water body and the non-water body.
5. The method for analyzing the water surface rate of the high-resolution image according to claim 1, wherein the extracting the target monitoring area through a data set to obtain an extraction result based on a data set standard, and determining an image shadow according to the first water distribution result and the extraction result comprises:
cutting FROM-GLC10 data including the target monitoring area;
re-classifying the FROM-GLC10 data, re-classifying the result through 3 × 3 convolution kernel corrosion, and further re-sampling the re-classified result to obtain a re-sampled image;
and intersecting the resampled image with the first water body distribution result to generate an initial shadow, corroding the initial shadow through a convolution core of 3 x 3, and eliminating small broken spots of the initial shadow by setting a small broken spot deleting threshold to obtain the fine image shadow.
6. The method for analyzing the high-resolution image water surface rate according to claim 1, wherein the determining the image water surface rate of the target monitoring area according to the second water body distribution result comprises:
corroding the obtained first water body result to remove burrs, deleting small broken spots, further performing vectorization, overlapping the vectorization with the remote sensing image, deleting and modifying error vectors, and supplementing a non-extraction area to obtain a second water body distribution result, wherein the second water body distribution result is used for representing an accurate water body range;
and calculating the accurate water area and the total area of the remote sensing image area, and determining the image water surface rate of the target monitoring area according to the ratio of the accurate water area to the total area of the remote sensing image area.
7. A high resolution image water surface rate analysis device is characterized by comprising:
the preprocessing module is used for responding to an analysis request, obtaining a high-resolution remote sensing image of a target monitoring area, and performing preprocessing on the high-resolution remote sensing image to obtain reflectivity data of the high-resolution remote sensing image, wherein the preprocessing corrects the high-resolution remote sensing image through preset parameters;
the extraction module is used for extracting the high-resolution images through the reflectivity data to obtain a first water body distribution result;
the image shadow module is used for extracting the target monitoring area through a data set to obtain an extraction result based on the data set standard, and determining an image shadow according to the first water body distribution result and the extraction result;
the accurate water area module is used for determining a second water body distribution result according to the first water body distribution result and the image shadow;
and the image water surface rate module is used for determining the image water surface rate of the target monitoring area according to the second water body distribution result.
8. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executes the program to realize the high-resolution image water surface rate analysis method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that the storage medium stores a program, the program being executed by a processor to implement the high-resolution image water surface rate analysis method according to any one of claims 1 to 6.
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| CN117557584B (en) * | 2024-01-10 | 2024-04-09 | 北京观微科技有限公司 | Water body extraction method and device, electronic equipment and storage medium |
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