CN115294455A - Remote sensing monitoring method, device, equipment and storage medium - Google Patents

Remote sensing monitoring method, device, equipment and storage medium Download PDF

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Publication number
CN115294455A
CN115294455A CN202210993101.8A CN202210993101A CN115294455A CN 115294455 A CN115294455 A CN 115294455A CN 202210993101 A CN202210993101 A CN 202210993101A CN 115294455 A CN115294455 A CN 115294455A
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remote sensing
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mining
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刘克俭
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PEOPLE'S PUBLIC SECURITY UNIVERSITY OF CHINA
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PEOPLE'S PUBLIC SECURITY UNIVERSITY OF CHINA
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/176—Urban or other man-made structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30184—Infrastructure

Abstract

The invention belongs to the technical field of remote sensing, and discloses a remote sensing monitoring method, a remote sensing monitoring device, remote sensing monitoring equipment and a storage medium. The method comprises the following steps: acquiring a remote sensing image of a target area; extracting a problem area from the target area according to the remote sensing image; determining the violation type corresponding to the problem area; and generating abnormality detection information according to the violation type, and sending the abnormality detection information to a preset terminal. According to the mode, the remote sensing image based on the target area automatically carries out periodic monitoring, the problem area can be determined based on the remote sensing image, then the violation type corresponding to the problem area is determined, the problem area can be accurately judged to belong to illegal construction or illegal exploitation, corresponding abnormal detection information is generated to give an early warning and a prompt to a user, the key area is automatically subjected to normalized monitoring, illegal construction or illegal exploitation targets are found in time, manpower, material resources and time investment are reduced, the efficiency is improved, and a basis is provided for law enforcement of relevant departments.

Description

Remote sensing monitoring method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of remote sensing, in particular to a remote sensing monitoring method, a remote sensing monitoring device, a remote sensing monitoring equipment and a storage medium.
Background
When the work of city management is carried out, the phenomenon that residents break the buildings in urban areas frequently, or illegal mining activities are carried out in suburbs, the safety of the cities or the protection of the environment are greatly influenced, the monitoring of the activities is extremely difficult, if the urban area breaking the buildings and the illegal mining are monitored only by means of mass inspection or personnel patrol, the situation that the monitoring is not timely can be caused, or the illegal mining and the illegal mining are difficult to find when the places where the building breaking and the illegal mining are hidden.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a remote sensing monitoring method, a remote sensing monitoring device, a remote sensing monitoring equipment and a remote sensing monitoring storage medium, and aims to solve the technical problem that the target of illegal construction and illegal exploitation in the prior art is difficult to accurately monitor.
In order to achieve the above object, the present invention provides a remote sensing monitoring method, comprising the steps of:
obtaining a remote sensing image of a target area;
extracting a problem area from the target area according to the remote sensing image;
determining the violation type corresponding to the problem area;
and generating abnormal detection information according to the violation type, and sending the abnormal detection information to a preset terminal.
Optionally, the extracting a problem region from the target region according to the remote sensing image includes:
determining periodic remote sensing images of the target area at all times according to the remote sensing images;
determining an image change area according to the periodic remote sensing image;
fitting each image change area, and determining the distribution information of each image change area in the remote sensing image;
determining the area coincidence rate of each image change area according to the distribution information;
and screening out a problem area from the image change area according to the area overlapping rate.
Optionally, the determining an image change area according to the periodic remote sensing image includes:
determining vector characteristic information of the remote sensing image of each period;
determining the vector change rate of the period remote sensing image between adjacent periods according to the vector characteristic information;
determining a plurality of vector mutation areas according to the vector characteristic information and the vector change rate;
and determining an image change area according to the vector mutation area.
Optionally, the determining a plurality of vector abrupt change regions according to the vector feature information and the vector change rate includes:
determining a plurality of preselected variation areas according to the vector characteristic information;
determining the vector net change rate of each pre-selected change area according to the vector change rate;
comparing the net change rate of the vector with a mutation threshold value to obtain a comparison result;
and selecting the preselected change area with the vector net change rate larger than the mutation threshold value as a vector mutation area according to the comparison result.
Optionally, the determining a violation type corresponding to the problem area includes:
determining the urban area proportion in each problem area;
judging the distance interval from each problem area to the center of the urban area according to the urban area ratio;
determining the geographical position information of each problem area according to the distance interval;
and determining the violation type corresponding to each problem area according to the geographical position information.
Optionally, the determining, according to the geographic location information, the violation type corresponding to each problem area includes:
judging the region position type of each problem region according to the geographical position information;
taking the problem area with the area position type of the city area as a default candidate area;
acquiring default vector characteristics of the default candidate region;
inputting the default vector features into a default judgment model to obtain default judgment results;
and determining an illegal building area from the illegal building candidate area according to the illegal building judgment result, and setting the illegal type of the illegal building area as the illegal building type.
Optionally, after the determining the area location type of each problem area according to the geographical location information, the method further includes:
taking the problem area with the area position type of suburban area as a mining alternative area;
acquiring mining vector characteristics of the mining candidate area;
comparing the mining vector characteristics with preset standard mining vector characteristics to obtain mining judgment results;
and determining a mining area from the mining candidate area according to the mining judgment result, and setting the violation type of the mining area as the violation type.
In addition, in order to achieve the above object, the present invention further provides a remote sensing monitoring apparatus, including:
the image acquisition module is used for acquiring a remote sensing image of a target area;
the region extraction module is used for extracting a problem region from the target region according to the remote sensing image;
the type determining module is used for determining the violation type corresponding to the problem area;
and the violation early warning module is used for generating abnormal detection information according to the violation type and sending the abnormal detection information to a preset terminal.
In addition, in order to achieve the above object, the present invention further provides a remote sensing monitoring apparatus, including: a memory, a processor and a telemetric monitoring program stored on said memory and executable on said processor, said telemetric monitoring program being configured to implement the steps of the telemetric monitoring method as described above.
In addition, to achieve the above object, the present invention further provides a storage medium, on which a remote sensing monitoring program is stored, and the remote sensing monitoring program implements the steps of the remote sensing monitoring method as described above when executed by a processor.
The method comprises the steps of obtaining a remote sensing image of a target area; extracting a problem area from the target area according to the remote sensing image; determining the violation type corresponding to the problem area; and generating abnormality detection information according to the violation type, and sending the abnormality detection information to a preset terminal. By the method, the remote sensing image based on the target area is automatically and periodically monitored, the problem area can be determined based on the remote sensing image, the violation type corresponding to the problem area is determined, the problem area can be accurately judged to be illegal or illegal, corresponding abnormal detection information is generated to give an early warning and a prompt to a user, the key area is automatically and normally monitored, the illegal or illegal exploitation target is timely found, manpower, material resources and time input are reduced, the efficiency is improved, and a basis is provided for relevant departments to enforce law.
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FIG. 1 is a schematic structural diagram of a remote sensing monitoring device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a remote sensing monitoring method according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating calculation of the area coincidence rate in an embodiment of the remote sensing monitoring method of the present invention;
FIG. 4 is a schematic flow chart of a remote sensing monitoring method according to a second embodiment of the present invention;
fig. 5 is a block diagram of the remote sensing monitoring apparatus according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a remote sensing monitoring device for a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the remote sensing monitoring apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of the telemetric monitoring device, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a remote sensing monitoring program.
In the remote sensing monitoring apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the remote sensing monitoring device of the present invention may be arranged in the remote sensing monitoring device, and the remote sensing monitoring device calls the remote sensing monitoring program stored in the memory 1005 through the processor 1001 and executes the remote sensing monitoring method provided by the embodiment of the present invention.
An embodiment of the present invention provides a remote sensing monitoring method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of a remote sensing monitoring method according to the present invention.
In this embodiment, the remote sensing monitoring method includes the following steps:
step S10: and acquiring a remote sensing image of the target area.
It should be noted that the execution subject in this embodiment may be a server, an entity server or a cloud server, or other devices capable of implementing this function, which is not limited in this embodiment.
It should be understood that, at present, the monitoring of illegal construction or illegal mining in urban areas is implemented by manual patrol monitoring, mass report or manual inspection of pictures shot by cameras in cities, but real-time and periodic automatic inspection cannot be guaranteed, and omission easily occurs. The remote sensing image based on the target area is automatically and periodically monitored, the problem area can be determined based on the remote sensing image, then the violation type corresponding to the problem area is determined, so that whether the problem area belongs to illegal construction or illegal exploitation can be accurately judged, corresponding abnormal detection information is generated to give an early warning and a prompt to a user, the key area is automatically and normally monitored, illegal construction or illegal exploitation targets are timely found, manpower, material resources and time investment are reduced, efficiency is improved, and a basis is provided for law enforcement of relevant departments.
In a specific implementation, the target area refers to an area under the monitoring range, and the position and the area size of the target area may be set by a user, which is not limited in this embodiment.
The remote sensing image includes a periodically acquired remote sensing image of the target area at each time.
Step S20: and extracting a problem area from the target area according to the remote sensing image.
It should be understood that the problem area refers to an area where an illegal activity, i.e., an illegal or illegal mining activity, may exist, as determined from the remotely sensed image.
Further, in order to accurately determine the problem area, step S20 includes: determining periodic remote sensing images of the target area at all times according to the remote sensing images; determining an image change area according to the periodic remote sensing image; fitting each image change area, and determining the distribution information of each image change area in the remote sensing image; determining the area coincidence rate of each image change area according to the distribution information; and screening out a problem area from the image change area according to the area coincidence rate.
In specific implementation, determining the periodic remote sensing image of the target area at each moment according to the remote sensing image refers to: remote sensing images at different moments of a target area are extracted from all remote sensing images, and then the remote sensing images are extracted periodically to obtain periodic remote sensing images in a fixed period. Wherein, the concrete length of fixed cycle can be set for by the user by oneself, for example: 1 day, 5 days, etc., which the present embodiment does not impose limitations.
The step of determining the image change area according to the periodic remote sensing images refers to fitting and comparing each periodic remote sensing image, determining vector characteristic information of each periodic remote sensing image, determining a vector change rate based on the vector characteristic information, and finally determining the image change area.
It should be understood that fitting each image change region and determining the distribution information of each image change region in the remote sensing image refers to: all the image change areas are identified on the target area, and then distribution, overlap, position information and the like of each image change area on the remote sensing image are obtained as distribution information.
In a specific implementation, determining the area overlapping rate of each image change area according to the distribution information includes: and determining the percentage of the area of the part, which is overlapped with other image change areas, in each image change area to the whole area according to the distribution information, and then taking the percentage as the area overlapping rate. Specifically, referring to fig. 3, a schematic diagram of calculating the area overlapping ratio is shown, where a rectangular area is a target area, and then all elliptical areas and circular areas are the respective image change areas, so that a shadow area is an overlapping portion of the image change areas, and therefore the area overlapping ratio is an area of the shadow portion divided by a total area of the image change areas.
The step of screening out the problem area from the image change area according to the area overlapping ratio is as follows: and taking the image change area with the area overlapping rate larger than the overlapping rate threshold value as a problem area. The coincidence rate threshold is a preset threshold, specifically, an arbitrary percentage threshold, which is not limited in this embodiment.
Through the method, the problem area is accurately screened out based on the remote sensing image, so that the violation type can be accurately judged and early warning is given to a user.
Further, in order to obtain an image change region based on the periodic remote sensing image, the step of determining the image change region from the periodic remote sensing image includes: determining vector characteristic information of the remote sensing image of each period; determining the vector change rate of the period remote sensing image between adjacent periods according to the vector characteristic information; determining a plurality of vector mutation areas according to the vector characteristic information and the vector change rate; and determining an image change area according to the vector mutation area.
It should be understood that the vector characteristic information refers to vector characteristic information obtained by subjecting all periodic remote sensing images to vectorization operation, and obtaining each periodic remote sensing image.
In a specific implementation, determining a vector change rate of the periodic remote sensing image between adjacent periods according to the vector feature information refers to: and calculating the vector change rate of each preset area of the periodic remote sensing images adjacent to each other in every two periods according to the vector characteristic information. Specifically, the adjacent cycle remote sensing images in every two cycles are two adjacent cycle remote sensing images at any moment. The preset area refers to a partition previously divided in the target area, and may be any number of preset partitions of any size.
The vector change rate is: and the percentage of the number of newly added or reduced vectors in all original vectors of the periodic remote sensing image compared with the original number. Specifically, the calculation formula of the vector change rate is as follows:
Figure BDA0003804602130000071
wherein Q is the vector change rate, A is the original vector number of the periodic remote sensing image, a 1 A newly added vector number for the period remote sensing image compared with the previous adjacent period remote sensing image b 1 The vector number of the periodic remote sensing image is reduced compared with the previous adjacent periodic remote sensing image. a is 2 A newly increased number of vectors for the periodic remote sensing image compared with the next adjacent periodic remote sensing image b 2 The vector number of the periodic remote sensing image is reduced compared with the subsequent adjacent periodic remote sensing image.
It should be understood that determining a plurality of vector mutation regions according to the vector feature information and the vector change rate refers to: determining a plurality of preselected change areas in the periodic remote sensing image, then calculating the vector net change rate of the preselected change areas, finally comparing the vector net change rate with a mutation threshold value to obtain a comparison result, and finally selecting the vector mutation areas from the preselected change areas based on the comparison result.
In specific implementation, after the vector mutation region is determined, the vector mutation region is labeled in a target region corresponding to the periodic remote sensing image to obtain an image change region.
By the method, the image change area is accurately defined, and the violation type is determined more accurately in the follow-up process.
Further, in order to accurately determine the vector mutation area, the step of determining a plurality of vector mutation areas according to the vector feature information and the vector change rate comprises: determining a plurality of preselected variation areas according to the vector characteristic information; determining the net change rate of the vector of each preselected change area according to the change rate of the vector; comparing the net change rate of the vector with a mutation threshold value to obtain a comparison result; and selecting the preselected change region with the vector net change rate larger than the mutation threshold value as a vector mutation region according to the comparison result.
It should be noted that, determining a plurality of preselected variation areas according to the vector feature information means: and determining a region in which the vector characteristics of the periodic remote sensing images in the same preset region continuously change as a preselected change region according to the vector characteristic information. Specifically, a sustained change refers to a region that changes over three cycles.
In a specific implementation, determining the net change rate of the vector of each preselected change area according to the change rate of the vector means: and calculating the vector change rate of each period of all the preselected change areas to obtain the average vector change rate, namely the vector net change rate.
The abrupt change threshold refers to a preset upper threshold of the net change rate of the vector, and may be a threshold of any value, which is not limited in this embodiment.
By the method, the vector abrupt change region can be accurately selected from the preselected change region, so that the image change region can be more accurately determined.
Step S30: and determining the violation type corresponding to the problem area.
It should be understood that determining the type of violation corresponding to the problem area refers to: determining the urban area proportion of the problem area, then judging the geographical position information of the problem area, and determining whether the violation type of each problem area is the violation building type or the violation mining type based on the geographical position information.
Step S40: and generating abnormal detection information according to the violation type, and sending the abnormal detection information to a preset terminal.
In specific implementation, after the violation type is determined, corresponding abnormal detection information is generated according to the violation type and then sent to a preset terminal. The abnormal detection information comprises the violation type, the specific violation position and the remote sensing image corresponding to the violation type. The preset terminal may be a smart phone, or may also be a computer, a tablet computer, or the like, which is not limited in this embodiment.
The embodiment obtains a remote sensing image of a target area; extracting a problem area from the target area according to the remote sensing image; determining the violation type corresponding to the problem area; and generating abnormal detection information according to the violation type, and sending the abnormal detection information to a preset terminal. By the method, the remote sensing image based on the target area is automatically and periodically monitored, the problem area can be determined based on the remote sensing image, the violation type corresponding to the problem area is determined, the problem area can be accurately judged to be illegal or illegal, corresponding abnormal detection information is generated to give an early warning and a prompt to a user, the key area is automatically and normally monitored, the illegal or illegal exploitation target is timely found, manpower, material resources and time input are reduced, the efficiency is improved, and a basis is provided for relevant departments to enforce law.
Referring to fig. 4, fig. 4 is a schematic flow chart of a remote sensing monitoring method according to a second embodiment of the present invention.
Based on the first embodiment, the remote sensing monitoring method of this embodiment includes, in step S30:
step S301: and determining the urban area proportion in each problem area.
It should be noted that the urban area ratio refers to: city ranges are fitted to the problem areas, and the percentage of city areas in each problem area is determined.
Step S302: and judging the distance interval from each problem area to the center of the urban area according to the urban area proportion.
It should be understood that, the judgment of the distance interval from each problem area to the center of the urban area according to the urban area ratio refers to: and comparing the urban area ratio with the distance interval mapping table to determine that the distance from the problem area to the urban center belongs to one of preset distance intervals. The distance interval mapping table is a preset comparison table in which the value intervals based on the urban area ratio correspond to different preset distance intervals. Wherein each percentage value from 0-100% corresponds to a unique predetermined distance interval.
Step S303: and determining the geographical position information of each problem area according to the distance interval.
In a specific implementation, determining the geographical location information of each problem area according to the distance interval means: determining relevant information of the problem area relative to the geographical location description of the city according to the distance interval, specifically, the geographical location description may be: city middle, suburb, outside city, etc.
Step S304: and determining the violation type corresponding to each problem area according to the geographical position information.
It should be noted that, determining the violation type corresponding to each problem area according to the geographic location information means: and judging the area position type of each problem area according to the geographical position information, and screening to obtain violation categories.
Further, in order to determine the corresponding violation building type when the problem area is a violation candidate area, step S204 includes: judging the region position type of each problem region according to the geographical position information; taking the problem area with the area position type of the city area as a default candidate area; acquiring default vector characteristics of the default candidate region; inputting the default vector features into a default judgment model to obtain default judgment results; and determining an illegal building area from the illegal building candidate area according to the illegal building judgment result, and setting the illegal type of the illegal building area as the illegal building type.
It should be understood that the judgment of the area location type of each problem area according to the geographical location information refers to: and determining the problem area as a default candidate area or a mining candidate area according to the geographic position information.
In specific implementation, the obtaining the default vector feature of the default candidate region refers to: and after all problem areas with the area position types of the urban areas are used as the illegal building candidate areas, extracting the vector characteristics of all illegal building candidate areas to obtain illegal building vector characteristics.
It should be noted that the default judgment model is a deep learning model obtained by pre-training, and whether the default candidate region corresponding to the default vector feature is performing default behavior can be judged according to the direct output result of the vector feature.
It should be understood that determining an illegal building area from the illegal building candidate area according to the illegal building judgment result and setting the illegal type of the illegal building area as the illegal building type refers to: and determining the illegal building candidate area of the illegal building behavior according to the illegal building judgment result, and taking the illegal building candidate area of the illegal building behavior as the illegal building area, wherein the illegal type corresponding to the illegal building area is the illegal building type.
By the method, the problem area is accurately screened when being the urban area, so that the area where the illegal action is carried out and the corresponding violation type are determined, and the targeted early warning is realized.
Further, in order to accurately identify the illegal mining behavior, step S204 includes: taking the problem area with the area position type of suburban area as a mining alternative area; acquiring mining vector characteristics of the mining candidate area; comparing the mining vector characteristics with preset standard mining vector characteristics to obtain mining judgment results; and determining a mining area from the mining candidate area according to the mining judgment result, and setting the violation type of the mining area as the violation type.
In a specific implementation, taking the problem area with the area location type of suburban area as a mining candidate area refers to: and extracting all problem areas with the area position types of suburban areas and using the problem areas as mining candidate areas.
It should be noted that the mining vector feature refers to a proper amount of features of the mining candidate region.
It should be understood that the standard mining vector refers to a vector feature of the pre-stored remote sensing images as mining activities are conducted. Comparing the mining vector characteristics with preset standard mining vector characteristics to obtain a mining judgment result, wherein the mining judgment result refers to: and comparing the mining vector features with the standard mining vector features, determining the similarity, and obtaining mining judgment results of all mining candidate areas based on the similarity.
In specific implementation, the step of determining a mining area from the mining candidate area according to the mining judgment result refers to: and taking the mining candidate area with the similarity higher than the lowest similarity value as the mining area according to the mining judgment result of the mining candidate area. The minimum value of the similarity is the minimum similarity of the preset vector features for determining that illegal mining behaviors are performed, and may be any value, which is not limited in this embodiment.
By the method, the problem area is accurately screened when the problem area is the mining alternative area, so that the mining area is accurately screened, and all problem areas with illegal mining types are determined.
In the embodiment, the urban area proportion in each problem area is determined; judging the distance interval from each problem area to the center of the urban area according to the urban area ratio; determining the geographical position information of each problem area according to the distance interval; and determining the violation type corresponding to each problem area according to the geographical position information. By the method, the geographical position information of each problem area can be accurately preliminarily deduced based on the urban area proportion, so that the specific violation type is determined to belong to illegal construction or illegal exploitation according to the position of the problem area relative to the city, the violation type can be more specifically judged, and the monitoring on the target area is more accurate.
In addition, an embodiment of the present invention further provides a storage medium, where a remote sensing monitoring program is stored on the storage medium, and the remote sensing monitoring program, when executed by a processor, implements the steps of the remote sensing monitoring method described above.
Since the storage medium adopts all technical solutions of all the above embodiments, at least all the beneficial effects brought by the technical solutions of the above embodiments are achieved, and details are not repeated here.
Referring to fig. 5, fig. 5 is a block diagram of a first embodiment of the remote sensing monitoring device according to the present invention.
As shown in fig. 5, a remote sensing monitoring apparatus according to an embodiment of the present invention includes:
and the image acquisition module 10 is used for acquiring a remote sensing image of the target area.
And the region extraction module 20 is used for extracting a problem region from the target region according to the remote sensing image.
And the type determining module 30 is configured to determine a violation type corresponding to the problem area.
And the violation early warning module 40 is configured to generate anomaly detection information according to the violation type, and send the anomaly detection information to a preset terminal.
The embodiment obtains a remote sensing image of a target area; extracting a problem area from the target area according to the remote sensing image; determining the violation type corresponding to the problem area; and generating abnormality detection information according to the violation type, and sending the abnormality detection information to a preset terminal. By the method, the remote sensing image based on the target area is automatically and periodically monitored, the problem area can be determined based on the remote sensing image, the violation type corresponding to the problem area is determined, the problem area can be accurately judged to belong to illegal construction or illegal exploitation, corresponding abnormal detection information is generated to give an early warning and a prompt to a user, the key area is automatically and normally monitored, the illegal construction or illegal exploitation target is timely found, manpower, material resources and time input are reduced, the efficiency is improved, and a basis is provided for law enforcement of relevant departments.
In an embodiment, the region extraction module 20 is further configured to determine, according to the remote sensing image, a periodic remote sensing image of the target region at each time; determining an image change area according to the periodic remote sensing image; fitting each image change area, and determining the distribution information of each image change area in the remote sensing image; determining the area coincidence rate of each image change area according to the distribution information; and screening out a problem area from the image change area according to the area coincidence rate.
In an embodiment, the region extraction module 20 is further configured to determine vector feature information of the remote sensing images in each period; determining the vector change rate of the period remote sensing image between adjacent periods according to the vector characteristic information; determining a plurality of vector mutation areas according to the vector characteristic information and the vector change rate; and determining an image change area according to the vector mutation area.
In an embodiment, the region extracting module 20 is further configured to determine a plurality of pre-selected variation regions according to the vector feature information; determining the vector net change rate of each pre-selected change area according to the vector change rate; comparing the net change rate of the vector with a mutation threshold value to obtain a comparison result; and selecting the preselected change region with the vector net change rate larger than the mutation threshold value as a vector mutation region according to the comparison result.
In an embodiment, the type determining module 30 is further configured to determine an urban area proportion in each problem area; judging the distance interval from each problem area to the center of the urban area according to the urban area ratio; determining the geographical position information of each problem area according to the distance interval; and determining the violation type corresponding to each problem area according to the geographical position information.
In an embodiment, the type determining module 30 is further configured to determine an area location type of each problem area according to the geographic location information; taking the problem area with the area position type being the urban area as a default candidate area; acquiring default vector characteristics of the default candidate region; inputting the illegal construction vector characteristics into an illegal construction judgment model to obtain an illegal construction judgment result; and determining an illegal building area from the illegal building alternative area according to the illegal building judgment result, and setting the illegal type of the illegal building area as the illegal building type.
In an embodiment, the type determining module 30 is further configured to use the problem area with the area location type being a suburban area as a mining candidate area; acquiring mining vector characteristics of the mining candidate area; comparing the mining vector characteristics with preset standard mining vector characteristics to obtain a mining judgment result; and determining a mining area from the mining candidate area according to the mining judgment result, and setting the violation type of the mining area as the violation type.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited in this respect.
It should be noted that the above-mentioned work flows are only illustrative and do not limit the scope of the present invention, and in practical applications, those skilled in the art may select some or all of them according to actual needs to implement the purpose of the solution of the present embodiment, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the remote sensing monitoring method provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A remote sensing monitoring method is characterized by comprising the following steps:
obtaining a remote sensing image of a target area;
extracting a problem area from the target area according to the remote sensing image;
determining the violation type corresponding to the problem area;
and generating abnormal detection information according to the violation type, and sending the abnormal detection information to a preset terminal.
2. The method of claim 1, wherein said extracting a problem area from said target area based on said remotely sensed image comprises:
determining a periodic remote sensing image of the target area at each moment according to the remote sensing image;
determining an image change area according to the periodic remote sensing image;
fitting each image change area, and determining the distribution information of each image change area in the remote sensing image;
determining the area coincidence rate of each image change area according to the distribution information;
and screening out a problem area from the image change area according to the area overlapping rate.
3. The method of claim 2, wherein said determining an image alteration area from said periodic remote sensing image comprises:
determining vector characteristic information of the remote sensing image of each period;
determining the vector change rate of the period remote sensing image between adjacent periods according to the vector characteristic information;
determining a plurality of vector mutation areas according to the vector characteristic information and the vector change rate;
and determining an image change area according to the vector mutation area.
4. The method of claim 3, wherein said determining a plurality of vector mutation areas based on said vector feature information and said vector rate of change comprises:
determining a plurality of preselected variation areas according to the vector characteristic information;
determining the vector net change rate of each pre-selected change area according to the vector change rate;
comparing the net change rate of the vector with a mutation threshold value to obtain a comparison result;
and selecting the preselected change region with the vector net change rate larger than the mutation threshold value as a vector mutation region according to the comparison result.
5. The method of claim 1, wherein the determining the type of violation corresponding to the problem area comprises:
determining the urban area proportion in each problem area;
judging the distance interval from each problem area to the center of the urban area according to the urban area ratio;
determining the geographical position information of each problem area according to the distance interval;
and determining the violation type corresponding to each problem area according to the geographical position information.
6. The method of claim 5, wherein the determining the violation type corresponding to each problem area according to the geographic location information comprises:
judging the area position type of each problem area according to the geographical position information;
taking the problem area with the area position type being the urban area as a default candidate area;
acquiring the illegal construction vector characteristics of the illegal construction alternative area;
inputting the default vector features into a default judgment model to obtain default judgment results;
and determining an illegal building area from the illegal building candidate area according to the illegal building judgment result, and setting the illegal type of the illegal building area as the illegal building type.
7. The method of claim 6, wherein after determining the area location type of each problem area based on the geographical location information, further comprising:
taking the problem area with the area position type of suburban area as a mining alternative area;
acquiring mining vector characteristics of the mining candidate area;
comparing the mining vector characteristics with preset standard mining vector characteristics to obtain mining judgment results;
and determining a mining area from the mining candidate area according to the mining judgment result, and setting the violation type of the mining area as the violation type.
8. A remote sensing monitoring device, comprising:
the image acquisition module is used for acquiring a remote sensing image of a target area;
the region extraction module is used for extracting a problem region from the target region according to the remote sensing image;
the type determining module is used for determining the violation type corresponding to the problem area;
and the violation early warning module is used for generating abnormality detection information according to the violation type and sending the abnormality detection information to a preset terminal.
9. A remote sensing monitoring device, the device comprising: a memory, a processor, and a telemetry monitor stored on the memory and executable on the processor, the telemetry monitor configured to implement the telemetry monitor method of any of claims 1 to 7.
10. A storage medium having stored thereon a remote monitoring program which, when executed by a processor, implements a remote monitoring method according to any one of claims 1 to 7.
CN202210993101.8A 2022-08-18 2022-08-18 Remote sensing monitoring method, device, equipment and storage medium Pending CN115294455A (en)

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CN110243354A (en) * 2019-07-04 2019-09-17 桂林理工大学 A kind of city illegal building object dynamic monitoring method and system
CN112990168A (en) * 2021-05-20 2021-06-18 江苏瞭望神州大数据科技有限公司 Illegal land monitoring method and system
CN114399692A (en) * 2022-01-13 2022-04-26 武汉微集思科技有限公司 Illegal construction identification monitoring detection method and system based on deep learning

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Publication number Priority date Publication date Assignee Title
US20120027298A1 (en) * 2010-07-27 2012-02-02 Aerotec, Llc Method and Apparatus for Direct Detection, Location, Analysis, Identification, and Reporting of Vegetation Clearance Violations
CN105444730A (en) * 2015-11-12 2016-03-30 中国矿业大学 Time-space characteristic and cross-border mining identification method for multi-source data monitoring mining area deformation
CN110243354A (en) * 2019-07-04 2019-09-17 桂林理工大学 A kind of city illegal building object dynamic monitoring method and system
CN112990168A (en) * 2021-05-20 2021-06-18 江苏瞭望神州大数据科技有限公司 Illegal land monitoring method and system
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