Remote Sensing Image Target Area Coverage Optimization Method, Device, Equipment and Medium

Abstract

The present invention discloses a method, device, equipment and medium for optimizing the coverage of a target area in remote sensing images. The present invention obtains the target area of the first contour and a number of candidate remote sensing images of the second contour, determines the target remote sensing images according to the first contour and the second contour, calculates the area of the unique part of each target remote sensing image. When the smallest unique part area is less than the redundancy threshold, the target remote sensing image corresponding to the smallest unique part area is deleted, and the step of calculating the area of the unique part of each target remote sensing image is returned until the smallest unique part area is greater than or equal to the redundancy threshold. When the smallest unique part area is greater than or equal to the redundancy threshold, a remote sensing image selection scheme is determined according to all the remaining target remote sensing images, reducing the number of finally retained target remote sensing images, optimizing the coverage of the target remote sensing images for the target area, reducing the manual participation, and improving the processing efficiency. The present invention can be widely applied to the field of remote sensing technology.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing, and in particular to a method, device, equipment and medium for optimizing the coverage of a target area in remote sensing images. Background Art

[0002] The processing and application of satellite remote sensing images are usually for a specific area. Usually, satellite remote sensing images are obtained by cameras. In the process of processing and applying various types of satellite remote sensing images, the finally selected image dataset should at least meet two requirements: one is to cover the entire target area as much as possible without omission; the other is that the number of images should be as small as possible to reduce the data purchase cost and data processing workload.

[0003] At present, the collection and selection of satellite remote sensing images are mainly completed manually. The operator first searches for remote sensing images related to the target area according to geographical information, then views these images in the form of manual visual quick views, selects appropriate data while taking into account area coverage and the number of images, and finally has to repeatedly adjust the arrangement order of the images so that the quality of the mosaicked image obtained after preprocessing reaches the best. This process is time-consuming and laborious. At the same time, with higher requirements for the resolution of remote sensing images, when covering the same target area, the number of high-resolution images required is much more than the number of original low-resolution images, which will further increase the difficulty of data selection work. Completing the collection and selection of satellite remote sensing images manually is more cumbersome and less efficient. Summary of the Invention

[0004] In view of this, in order to solve at least one of the above technical problems, the purpose of the present invention is to provide a method, device, equipment and medium for optimizing the coverage of a target area in remote sensing images to improve efficiency.

[0005] The technical solution adopted in the embodiments of the present invention is as follows:

[0006] A method for optimizing the coverage of a target area in remote sensing images includes:

[0007] Obtaining a target area of a first contour and a plurality of candidate remote sensing images of second contours;

[0008] Determining target remote sensing images according to the first contour and the second contour;

[0009] Calculating the area of the unique part of each target remote sensing image; the area of the unique part is the area of the non-intersecting part of each target remote sensing image and other target remote sensing images;

[0010] When the area of the smallest unique part is less than the duplication threshold, delete the target remote sensing image corresponding to the area of the smallest unique part, and return to the step of calculating the area of the unique part of each target remote sensing image until the area of the smallest unique part is greater than or equal to the duplication threshold;

[0011] When the area of the smallest unique part is greater than or equal to the duplication threshold, determine the remote sensing image selection scheme according to all the remaining target remote sensing images.

[0012] Further, the obtaining the target area of the first contour and the candidate remote sensing images of several second contours includes:

[0013] Obtain the target area and determine the first longitude coordinate value and the first latitude coordinate value of each first vertex of the target area. According to the maximum first longitude coordinate value, the maximum first latitude coordinate value, the minimum first longitude coordinate value, and the minimum first latitude coordinate value, determine the first contour;

[0014] Obtain the candidate remote sensing images containing the target area from the remote sensing image database, and determine the second longitude coordinate value and the second latitude coordinate value of each second vertex of each candidate remote sensing image to determine the second contour.

[0015] Further, the determining the target remote sensing image according to the first contour and the second contour includes:

[0016] Determine whether each second contour and the first contour have a first intersection;

[0017] Determine the target remote sensing image according to the candidate remote sensing images of all the second contours having the first intersection.

[0018] Further, the calculating the area of the unique part of each target remote sensing image includes:

[0019] Obtain the third contour of each target remote sensing image;

[0020] Calculate the second intersection of each third contour and other third contours;

[0021] Calculate the union of each second intersection and the overlapping area corresponding to the union;

[0022] Calculate the effective part area of each third contour, and determine the difference between each effective part area and the corresponding overlapping area to obtain the area of the unique part of each target remote sensing image.

[0023] Further, the when the area of the smallest unique part is less than the duplication threshold, deleting the target remote sensing image corresponding to the area of the smallest unique part includes:

[0024] Sort the target remote sensing images in descending order according to the area of the unique part;

[0025] Take the area of the unique part corresponding to the target remote sensing image with the last sorting result as the minimum area of the unique part; where when the number of minimum values of the area of the unique part corresponding to the target remote sensing image is more than two, take the target remote sensing image with the minimum effective part area among those with the minimum value as the target remote sensing image with the last sorting result;

[0026] When the minimum area of the unique part is less than the duplication threshold, delete the target remote sensing image corresponding to the minimum area of the unique part.

[0027] Further, the target remote sensing image has a corresponding name. When the minimum area of the unique part is greater than or equal to the duplication threshold, determine a remote sensing image selection scheme according to all the remaining target remote sensing images, including:

[0028] Output and display the sorting and the name of the remaining target remote sensing images; the remote sensing image selection scheme includes the name and the sorting.

[0029] Further, when the minimum area of the unique part is greater than or equal to the duplication threshold, determining a remote sensing image selection scheme according to all the remaining target remote sensing images further includes:

[0030] When the minimum area of the unique part is greater than or equal to the duplication threshold, calculate the total area of all the remaining target remote sensing images and the area of the target region;

[0031] Determine the coverage according to the ratio of the total area to the area of the target region; the remote sensing image selection scheme includes the coverage.

[0032] An embodiment of the present invention further provides a remote sensing image target area coverage optimization device, including:

[0033] An acquisition module, configured to acquire a target area of a first contour and several candidate remote sensing images of second contours;

[0034] A determination module, configured to determine a target remote sensing image according to the first contour and the second contour;

[0035] A calculation module, configured to calculate the area of the unique part of each target remote sensing image; the area of the unique part is the area of the non-intersecting part of each target remote sensing image and other target remote sensing images;

[0036] A processing module, configured to delete the target remote sensing image corresponding to the area of the smallest unique part when the area of the smallest unique part is less than the duplication threshold, and return to the step of calculating the area of the unique part of each target remote sensing image until the area of the smallest unique part is greater than or equal to the duplication threshold;

[0037] When the area of the smallest unique part is greater than or equal to the duplication threshold, determine a remote sensing image selection scheme according to all the remaining target remote sensing images.

[0038] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method.

[0039] An embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method.

[0040] The beneficial effects of the present invention are as follows: By obtaining the target area of the first contour and several candidate remote sensing images of the second contour, determining the target remote sensing image according to the first contour and the second contour, calculating the area of the unique part of each target remote sensing image, when the area of the smallest unique part is less than the duplication threshold, deleting the target remote sensing image corresponding to the area of the smallest unique part, and returning to the step of calculating the area of the unique part of each target remote sensing image until the area of the smallest unique part is greater than or equal to the duplication threshold. When the area of the smallest unique part is greater than or equal to the duplication threshold, determining a remote sensing image selection scheme according to all the remaining target remote sensing images, reducing the number of finally retained target remote sensing images, optimizing the coverage of the target remote sensing images for the target area, reducing manual participation, and improving the processing efficiency. Description of the Drawings

[0041] Figure 1 It is a schematic flowchart of the steps of the method for optimizing the coverage of the target area of remote sensing images according to the present invention;

[0042] Figure 2 It is a schematic display diagram of the remote sensing image selection scheme in a specific embodiment of the present invention. Detailed Embodiment

[0043] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solution in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0044] The terms "first", "second", "third", "fourth", etc. in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0045] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0046] As Figure 1 shown, an embodiment of the present invention provides a method for optimizing the coverage of a target area in remote sensing images, including steps S100 - S500, where steps S400 and S500 may only include step S500 or include both steps S400 and S500 simultaneously:

[0047] S100. Obtain the target area of the first contour and several candidate remote sensing images of the second contour.

[0048] Optionally, step S100 includes steps S110 - S120:

[0049] S110. Obtain the target area and determine the first longitude coordinate values and the first latitude coordinate values of each first vertex of the target area. According to the maximum first longitude coordinate value, the maximum first latitude coordinate value, the minimum first longitude coordinate value, and the minimum first latitude coordinate value, determine the first contour.

[0050] In the embodiments of the present invention, the target area includes, but is not limited to, areas such as construction areas, desert areas, forestry survey areas, etc., which can be adjusted as needed. Optionally, the target area is determined by combining the position of the target area in the map to determine each first vertex of the target area. For example, assuming that the target area is a polygon, the first longitude coordinate value and the first latitude coordinate value of the first vertex are determined, so as to calculate and determine the first contour according to the maximum first longitude coordinate value, the maximum first latitude coordinate value, the minimum first longitude coordinate value, and the minimum first latitude coordinate value, that is, the area enclosed by each side of the polygon, and obtain the target area of the first contour.

[0051] S120. Obtain candidate remote sensing images including the target area from the remote sensing image database, and determine the second longitude coordinate value and the second latitude coordinate value of each second vertex of each candidate remote sensing image to determine the second contour.

[0052] In the embodiments of the present invention, candidate remote sensing images including the target area are obtained by a remote sensing device and saved in the remote sensing image database. Similarly, the second longitude coordinate value and the second latitude coordinate value of each second vertex of the target area are determined in combination with the map. In the embodiments of the present invention, taking the candidate remote sensing image as a quadrilateral as an example, the second vertex is the four vertices of the quadrilateral, and the second contour is determined according to the second longitude coordinate value and the second latitude coordinate value of the four second vertices, that is, the area enclosed by each side of the quadrilateral, and the candidate remote sensing image of the second contour is obtained.

[0053] S200. Determine the target remote sensing image according to the first contour and the second contour.

[0054] Optionally, step S200 includes steps S210-S220:

[0055] S210. Determine whether each second contour and the first contour have a first intersection.

[0056] S220. Determine the target remote sensing image according to the candidate remote sensing images of all second contours having the first intersection.

[0057] In the embodiments of the present invention, since the candidate remote sensing images may include other redundant parts of non-target areas, if the redundant parts are retained, the processing difficulty will be increased and the processing efficiency will be affected in subsequent processing. Therefore, it is necessary to crop the invalid parts. Specifically, it is determined whether each second contour has a first intersection with the first contour. If there is no first intersection, it means that all redundant parts are deleted. If there is a first intersection, the candidate remote sensing images with the second contours having the first intersection are retained, and the invalid parts in the retained candidate remote sensing images are deleted to obtain the target remote sensing images. For example, when the retained candidate remote sensing images have part A in the first intersection and part B not in the first intersection, part B is deleted, and part A of the retained candidate remote sensing images is used as a part of the target remote sensing images.

[0058] S300. Calculate the area of the unique part of each target remote sensing image.

[0059] It should be noted that the area of the unique part is the area of the non-intersecting part of each target remote sensing image and other target remote sensing images, and the intersecting part of each target remote sensing image and other target remote sensing images is the overlapping part, and the area of the overlapping part is denoted as the overlapping part area.

[0060] Optionally, step S300 includes steps S310 - S340:

[0061] S310. Obtain the third contour of each target remote sensing image.

[0062] It should be noted that the determination method of the third contour is similar to that in step S100 and will not be elaborated here.

[0063] S320. Calculate the second intersection of each third contour and other third contours.

[0064] S330. Calculate the union of each second intersection and the overlapping part area corresponding to the union.

[0065] It should be noted that the overlapping part area corresponding to each union is the total area of the overlapping parts of each third contour and all other third contours.

[0066] S340. Calculate the effective part area of each third contour, and determine the difference between each effective part area and the corresponding overlapping part area to obtain the area of the unique part of each target remote sensing image.

[0067] Specifically, calculate the effective part area of each third contour (i.e., the area of the region enclosed by the third contour), calculate the difference between each effective part area and the overlapping part area of the third contour corresponding to the effective part area to obtain the area of the unique part of each target remote sensing image.

[0068] S400. When the area of the smallest unique part is less than the duplication threshold, delete the target remote sensing image corresponding to the area of the smallest unique part, and return to the step of calculating the area of the unique part of each target remote sensing image until the area of the smallest unique part is greater than or equal to the duplication threshold.

[0069] Optionally, step S400 includes steps S410 - S430:

[0070] S410. Sort the target remote sensing images in descending order according to the area of the unique part.

[0071] Optionally, in the embodiment of the present invention, the target remote sensing images are arranged from top to bottom according to the size of the area of the unique part; in other embodiments, they can be arranged from bottom to top or left and right, without specific limitation.

[0072] S420. Take the area of the unique part corresponding to the target remote sensing image sorted last as the area of the smallest unique part.

[0073] It should be noted that when the number of minimum values of the area of the unique part corresponding to the target remote sensing image is one, arrange the target remote sensing image corresponding to this minimum value last; when the number of minimum values of the area of the unique part corresponding to the target remote sensing image is two or more, take the target remote sensing image with the minimum value and the smallest effective part area as the target remote sensing image sorted last. It can be understood that during the process of arranging the target remote sensing images from top to bottom, similarly when the areas of the unique parts of the target remote sensing images are the same, they are arranged from top to bottom according to the effective part area.

[0074] S430. When the area of the smallest unique part is less than the duplication threshold, delete the target remote sensing image corresponding to the area of the smallest unique part.

[0075] Among them, after deleting the target remote sensing image corresponding to the area of the smallest unique part, return to the step of calculating the area of the unique part of each target remote sensing image, that is, return to step S300, until the area of the smallest unique part is greater than or equal to the duplication threshold.

[0076] S500. When the area of the smallest unique part is greater than or equal to the duplication threshold, determine the remote sensing image selection scheme according to all the remaining target remote sensing images.

[0077] Optionally, when obtaining candidate remote sensing images, the candidate remote sensing images have corresponding names, such as serial numbers, words, numbers, etc., so that the target remote sensing images also retain the corresponding names, that is, the names of the candidate remote sensing images. It should be noted that the duplication threshold can be set according to the actual situation. When there are no target remote sensing images to be deleted in step S400, all the retained target remote sensing images are the target remote sensing images determined in step S200. When there are target remote sensing images to be deleted in step S400, all the retained target remote sensing images refer to the target remote sensing images remaining after deleting the target remote sensing images in S400 from the target remote sensing images determined in S200.

[0078] Optionally, step S500 includes step S510 and S520, and the execution order of S510 and S520 is not limited:

[0079] S510. Output and display the sorting and names of the retained target remote sensing images.

[0080] Specifically, when the area of the smallest unique part is greater than or equal to the duplication threshold, that is, all the currently retained target remote sensing images are the target areas with the smallest quantity and can effectively cover the target area, output and display the sorting of the currently retained target remote sensing images (that is, the order arranged according to the size of the unique part area) and the names of the unique part areas. It should be noted that the remote sensing image selection scheme includes names and sorting.

[0081] S520. When the area of the smallest unique part is greater than or equal to the duplication threshold, calculate the total area of all the retained target remote sensing images and the area of the target area, and determine the coverage according to the ratio of the total area to the area of the target area.

[0082] Optionally, when the area of the smallest unique part is greater than or equal to the duplication threshold, calculate the total area of all the retained target remote sensing images at this time and the area of the target area (that is, the area enclosed by the first contour), and determine the coverage according to the ratio of the total area to the area of the target area. It can be understood that the greater the coverage, the more comprehensive the coverage of the target remote sensing images. And in the embodiments of the present invention, as few target remote sensing images as possible are retained, so the effect of well covering the target area with a small number of target remote sensing images is achieved.

[0083] It should be noted that in the embodiments of the present invention, the remote sensing image selection scheme includes coverage, sorting, and names, and the remote sensing image selection scheme is displayed, so that relevant personnel can understand the remote sensing image selection scheme and provide an optimized decision reference when selecting remote sensing image data for the target area.

[0084] Such as Figure 2As shown, it is a schematic diagram of the display page of the remote sensing image selection scheme. In area 100, the target area 102 and several target remote sensing images 102 are displayed. In the display area 200, contents such as the name, sorting, and coverage of the target remote sensing images can be displayed. The function area 300 allows relevant personnel to operate functions such as importing, exporting data, querying data, and adjusting area 100 and display area 200.

[0085] The remote sensing image target area coverage optimization method according to the embodiment of the present invention is applicable to various types of satellite remote sensing images, target areas of concern, processing and application tasks, and can automatically and quickly find a data set (the finally retained target remote sensing images) that can cover the target area and has the least number of images, and output and display an optimized data selection scheme that can replace manual operations.

[0086] The embodiment of the present invention also provides a remote sensing image target area coverage optimization device, including:

[0087] An acquisition module, configured to acquire the target area of the first contour and several candidate remote sensing images of the second contour;

[0088] A determination module, configured to determine the target remote sensing images according to the first contour and the second contour;

[0089] A calculation module, configured to calculate the area of the unique part of each target remote sensing image; the area of the unique part is the area of the non-intersecting part of each target remote sensing image and other target remote sensing images;

[0090] A processing module, configured to, when the smallest area of the unique part is less than the redundancy threshold, delete the target remote sensing image corresponding to the smallest area of the unique part, and return to the step of calculating the area of the unique part of each target remote sensing image until the smallest area of the unique part is greater than or equal to the redundancy threshold;

[0091] When the smallest area of the unique part is greater than or equal to the redundancy threshold, determine the remote sensing image selection scheme according to all the retained target remote sensing images.

[0092] The contents in the above method embodiments are all applicable to the device embodiments of the present invention. The functions specifically implemented by the device embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0093] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the remote sensing image target area coverage optimization method of the foregoing embodiment. The electronic device of the embodiment of the present invention includes, but is not limited to, any intelligent terminal such as a mobile phone, a tablet computer, a computer, and an in-vehicle computer.

[0094] The content in the above method embodiment is applicable to the device embodiment of the present invention. The functions specifically implemented by the device embodiment of the present invention are the same as those of the above method embodiment, and the beneficial effects achieved are also the same as those of the above method embodiment.

[0095] An embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the remote sensing image target area coverage optimization method of the foregoing embodiment.

[0096] An embodiment of the present invention further provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the remote sensing image target area coverage optimization method of the foregoing embodiment.

[0097] Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0098] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expressions refer to any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a, b, and c", where a, b, and c can be single or multiple.

[0099] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0100] When an integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0101] The above, the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of this application.

Claims

1. Remote sensing image target area coverage optimization method, characterized in that Including: Obtaining candidate remote sensing images of the target area of the first contour and several second contours; Determining the target remote sensing image according to the first contour and the second contour; Calculating the unique part area of each target remote sensing image; the unique part area is the area of the non-intersecting part of each target remote sensing image and other target remote sensing images; When the smallest unique part area is less than the repetition threshold, deleting the target remote sensing image corresponding to the smallest unique part area, and returning to the step of calculating the unique part area of each target remote sensing image until the smallest unique part area is greater than or equal to the repetition threshold; When the smallest unique part area is greater than or equal to the repetition threshold, determining a remote sensing image selection scheme according to all the remaining target remote sensing images; Among them, the determining the remote sensing image selection scheme according to all the remaining target remote sensing images includes: calculating the total area of all the remaining target remote sensing images and the area of the target area; determining the coverage according to the ratio of the total area to the area of the target area; the remote sensing image selection scheme includes the coverage.

2. The remote sensing image target area coverage optimization method according to claim 1, characterized in that: The obtaining candidate remote sensing images of the target area of the first contour and several second contours includes: Obtaining the target area and determining the first longitude coordinate value and the first latitude coordinate value of each first vertex of the target area, and determining the first contour according to the maximum first longitude coordinate value, the maximum first latitude coordinate value, the minimum first longitude coordinate value and the minimum first latitude coordinate value; Obtaining candidate remote sensing images containing the target area from the remote sensing image database, and determining the second longitude coordinate value and the second latitude coordinate value of each second vertex of each candidate remote sensing image to determine the second contour.

3. The remote sensing image target area coverage optimization method according to claim 1, characterized in that: The determining the target remote sensing image according to the first contour and the second contour includes: Determining whether each second contour and the first contour have a first intersection; Determining the target remote sensing image according to the candidate remote sensing images of all the second contours having the first intersection.

4. The remote sensing image target area coverage optimization method according to claim 1, wherein: The calculating the unique part area of each target remote sensing image includes: Obtaining the third contour of each target remote sensing image; Calculating the second intersection of each third contour and other third contours; Calculating the union of each second intersection and the overlapping part area corresponding to the union; Calculating the effective part area of each third contour, and determining the difference between each effective part area and the corresponding overlapping part area to obtain the unique part area of each target remote sensing image.

5. The remote sensing image target area coverage optimization method according to claim 4, wherein: The when the smallest unique part area is less than the repetition threshold, deleting the target remote sensing image corresponding to the smallest unique part area includes: Sorting the target remote sensing images in descending order according to the unique part area; Taking the unique part area corresponding to the target remote sensing image sorted last as the smallest unique part area; where when the number of minimum values of the unique part area corresponding to the target remote sensing image is more than two, taking the target remote sensing image with the minimum value and the smallest effective part area as the target remote sensing image sorted last; When the area of the smallest unique part is less than the duplication threshold, delete the target remote sensing image corresponding to the area of the smallest unique part.

6. The remote sensing image target area coverage optimization method according to claim 5, characterized in that: The target remote sensing image has a corresponding name. When the area of the smallest unique part is greater than or equal to the duplication threshold, determine a remote sensing image selection scheme according to all the remaining target remote sensing images, including: Output and display the sorting and the name of the remaining target remote sensing images; the remote sensing image selection scheme includes the name and the sorting.

7. An optimization device for covering a target area of a remote sensing image, characterized in that, including: An acquisition module, configured to acquire a target area of a first contour and a plurality of candidate remote sensing images of second contours; A determination module, configured to determine a target remote sensing image according to the first contour and the second contour; A calculation module, configured to calculate the area of the unique part of each target remote sensing image; the area of the unique part is the area of the non-intersecting part of each target remote sensing image and other target remote sensing images; A processing module, configured to delete the target remote sensing image corresponding to the area of the smallest unique part when the area of the smallest unique part is less than the duplication threshold, and return to the step of calculating the area of the unique part of each target remote sensing image until the area of the smallest unique part is greater than or equal to the duplication threshold; When the area of the smallest unique part is greater than or equal to the duplication threshold, determine a remote sensing image selection scheme according to all the remaining target remote sensing images; wherein, determining a remote sensing image selection scheme according to all the remaining target remote sensing images includes: calculating the total area of all the remaining target remote sensing images and the area of the target area; determining the coverage according to the ratio of the total area to the area of the target area; the remote sensing image selection scheme includes the coverage.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, At least one instruction, at least one program, a code set or an instruction set is stored in the storage medium. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method according to any one of claims 1-6.

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