A method for automatically selecting multi-temporal remote sensing satellite images covering a large area

Abstract

The application relates to a method for automatically selecting satellite images in a large area and in multiple time phases, and belongs to the field of optical remote sensing satellite image applications. The method solves the problems of low efficiency in selecting satellite images for final mosaicking, inability to guarantee that there is no redundant satellite image in the selected satellite image subset, and frequent omission, which leads to the problem that the satellite image coverage rate of a large area cannot reach the maximum. Through a selection method based on the geometric operation of the four corner point coordinates of optical remote sensing satellite images and the coverage range of a large area, an optical remote sensing satellite image subset which can guarantee the maximum coverage rate and has no redundant coverage for the large area is automatically selected from the optical remote sensing satellite image set in multiple time phases. The method is also suitable for application in the field of selecting an optical remote sensing satellite image subset which can guarantee the maximum coverage rate and has no redundant coverage for the large area.

Description

Technical Field

[0001] This invention relates to the field of optical remote sensing satellite imagery application technology, specifically to a method for automatically selecting coverage of multi-temporal remote sensing satellite images over a large area. Background Technology

[0002] With the launch and operation of numerous optical remote sensing satellites in recent years, the ability to acquire optical remote sensing satellite imagery has significantly improved. When mosaicking large-area satellite imagery, multiple temporal images often exist within the region, covering more than one layer. However, in practice, mosaicking large-area satellite imagery only requires one layer of non-redundant imagery covering the entire area. Therefore, it is necessary to select a subset of satellite images from the multi-temporal, multi-layered remote sensing satellite imagery set that ensures maximum coverage of the large area without redundancy. Traditional satellite image selection relies mainly on manual screening of each image sheet, often requiring the selection of the final imagery for mosaicking from a set of satellite images several times larger than the actual selection result. This process is extremely time-consuming and cannot guarantee that the selected subset of satellite images is free of redundancy, often resulting in omissions and insufficient coverage of the large area. Summary of the Invention

[0003] This invention addresses the problem in existing technologies where satellite image selection relies primarily on manual, image-by-image screening. This often requires choosing the final image for mosaicking from a dataset several times larger than the actual selection result. This process is extremely time-consuming and cannot guarantee that the selected subset of satellite images is free of redundancy. Furthermore, it frequently results in omissions, leading to insufficient satellite image coverage over large areas.

[0004] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0005] Option 1: This invention proposes an automatic selection method for multi-temporal remote sensing satellite image coverage over a large area, the method comprising the following steps:

[0006] Step 1: Initialize the set P of all multi-temporal optical remote sensing satellite images, the selected optical remote sensing satellite images Ps, the set Pr of the remaining unselected optical remote sensing satellite images, the coverage area Gs of the selected optical remote sensing satellite images, and the large area G.

[0007] Step 2: Mark all remaining unselected optical remote sensing satellite images in the Pr set as unprocessed;

[0008] Step 3: If there is no image in the remaining unselected optical remote sensing satellite image set Pr that is in an unprocessed state, then proceed to step 4. Otherwise, select any image Pi in the remaining unselected optical remote sensing satellite image set Pr that is in an unprocessed state, calculate the intersection area of ​​Gs and Pi based on the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi, and change the state of Pi to processed.

[0009] Step 4: Select the image Pm with a non-zero intersection area and the smallest intersection area from the remaining unselected optical remote sensing satellite image set Pr. If there is no image Pm with a non-zero intersection area and the smallest intersection area, then select any Pi with a zero intersection area as Pm, let Gs = Gs∪Pm's coverage area, and add the image Pm with a non-zero intersection area and the smallest intersection area to the selected optical remote sensing satellite image Ps. Remove Pm from the remaining unselected optical remote sensing satellite image set Pr.

[0010] Step 5: For the selected optical remote sensing satellite image Ps, determine whether some images other than Pm in the current Ps become redundant due to the addition of Pm in step 4.

[0011] Step 6: Determine whether the remaining unselected optical remote sensing satellite image set Pr is empty or whether the coverage area Gs of the selected optical remote sensing satellite image contains a large area G. If so, output the currently selected optical remote sensing satellite image set Ps, that is, the subset of optical remote sensing satellite images with the largest coverage and no redundant coverage; otherwise, return to step 2.

[0012] Furthermore, a preferred embodiment is provided, wherein step 3, calculating the intersection area of ​​Gs and Pi based on the positional relationship between the selected optical remote sensing satellite image coverage area Gs and the unprocessed image Pi, includes:

[0013] When Gs intersects Pi, calculate the area of ​​the intersection of Gs and Pi, change the state of Pi to processed, and return to re-execute step 3;

[0014] When Gs and Pi are separated, set the area of ​​the intersection of Gs and Pi to 0, change the state of Pi to processed, and return to re-execute step 3;

[0015] When Gs contains Pi, remove Pi from Pr and return to step 3 again;

[0016] When P i If Gs is included, replace Gs with the coverage area of ​​Pi, clear the current Ps, put Pi into Ps, remove Pi from Pr, and return to re-execute step 3.

[0017] Furthermore, a preferred embodiment is provided, wherein the method for determining in step 5 whether certain images in the current Ps, excluding Pm, become redundant due to the addition of Pm in step 4 is as follows:

[0018] Step 5.1: Initialize by marking all images in the current Ps as redundant;

[0019] Step 5.2: Determine whether there are any images marked as redundant in the current Ps. If there are, proceed to step 5.3; otherwise, end step 5.

[0020] Step 5.3: Select any image Psi from the currently marked as redundant images, calculate the coverage area Gsi of all images in Ps except Psi. If Gsi can completely contain the coverage area of ​​Psi, then the current Psi is a redundant image and it is removed from Ps. Otherwise, mark it as non-redundant and return to step 5.2.

[0021] Furthermore, a preferred embodiment is provided, in step 4, the image Pm with a non-zero intersection area and the smallest intersection area is selected from the remaining unselected optical remote sensing satellite image set Pr, which is obtained by the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi.

[0022] Furthermore, a preferred embodiment is provided, wherein the initialization method in step 1 is as follows: P is the set of all multi-temporal optical remote sensing satellite images, G is the large area range, the selected optical remote sensing satellite image set Ps is an empty set, the coverage range Gs of the selected optical remote sensing satellite images is an empty set, and the remaining unselected optical remote sensing satellite image set Pr is equal to the multi-temporal optical remote sensing satellite image set P, when initialization is performed.

[0023] Option 2: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in any one of Options 1.

[0024] Option 3: A computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the method described in any one of Options 1.

[0025] Option 4: A computer program product, which, when executed, implements the method described in Option 1.

[0026] The advantages of this invention are:

[0027] This invention proposes a method for geometric calculation based on the coordinates of the four corner points of optical remote sensing satellite images and the coverage of a large area. This method automatically selects a subset of optical remote sensing satellite images from these multi-temporal optical remote sensing satellite image sets that can guarantee the maximum coverage of the large area without redundant coverage.

[0028] This invention is also applicable to the application of selecting a subset of optical remote sensing satellite imagery that can guarantee maximum coverage and avoid redundant coverage for a large area. Attached Figure Description

[0029] Figure 1 A flowchart illustrating an automatic selection method for multi-temporal remote sensing satellite image coverage over a large area, as described in Implementation Method 1.

[0030] Figure 2 This is a schematic diagram of the redundant image judgment process described in Implementation Method 1.

[0031] Figure 3 This is a schematic diagram of the initial query results as described in Implementation Method Nine.

[0032] Figure 4 This is a diagram showing the result after using the coverage optimization algorithm as described in Implementation Method Nine.

[0033] Figure 5 This is a schematic diagram illustrating the result of optimizing the latest coverage based on the shooting time as described in Implementation Method Nine.

[0034] Figure 6 This is a schematic diagram illustrating the optimized result based on the maximum coverage area per single shot, as described in Implementation Method Nine. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0036] Implementation Method 1: This implementation method provides an automatic selection method for multi-temporal remote sensing satellite imagery coverage over a large area. The method includes the following steps:

[0037] Step 1: Initialize the set P of all multi-temporal optical remote sensing satellite images, the selected optical remote sensing satellite images Ps, the set Pr of the remaining unselected optical remote sensing satellite images, the coverage area Gs of the selected optical remote sensing satellite images, and the large area G.

[0038] Step 2: Mark all remaining unselected optical remote sensing satellite images in the Pr set as unprocessed;

[0039] Step 3: If there is no image in the remaining unselected optical remote sensing satellite image set Pr that is in an unprocessed state, then proceed to step 4. Otherwise, select any image Pi in the remaining unselected optical remote sensing satellite image set Pr that is in an unprocessed state, calculate the intersection area of ​​Gs and Pi based on the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi, and change the state of Pi to processed.

[0040] Step 4: Select the image Pm with a non-zero intersection area and the smallest intersection area from the remaining unselected optical remote sensing satellite image set Pr. If there is no image Pm with a non-zero intersection area and the smallest intersection area, then select any Pi with a zero intersection area as Pm, let Gs = Gs∪Pm's coverage area, and add the image Pm with a non-zero intersection area and the smallest intersection area to the selected optical remote sensing satellite image Ps. Remove Pm from the remaining unselected optical remote sensing satellite image set Pr.

[0041] Step 5: For the selected optical remote sensing satellite image Ps, determine whether some images other than Pm in the current Ps become redundant due to the addition of Pm in step 4.

[0042] Step 6: Determine whether the remaining unselected optical remote sensing satellite image set Pr is empty or whether the coverage area Gs of the selected optical remote sensing satellite image contains a large area G. If so, output the currently selected optical remote sensing satellite image set Ps, that is, the subset of optical remote sensing satellite images with the largest coverage and no redundant coverage; otherwise, return to step 2.

[0043] Implementation Method 2: This implementation method further defines the automatic selection method for multi-temporal remote sensing satellite image coverage over a large area described in Implementation Method 1. In step 3, the intersection area of ​​Gs and Pi is calculated based on the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi.

[0044] When Gs intersects Pi, calculate the area of ​​the intersection of Gs and Pi, change the state of Pi to processed, and return to re-execute step 3;

[0045] When Gs and Pi are separated, set the area of ​​the intersection of Gs and Pi to 0, change the state of Pi to processed, and return to re-execute step 3;

[0046] When Gs contains Pi, remove Pi from Pr and return to step 3 again;

[0047] When P iIf Gs is included, replace Gs with the coverage area of ​​Pi, clear the current Ps, put Pi into Ps, remove Pi from Pr, and return to re-execute step 3.

[0048] Implementation Method 3: This implementation method further defines the automatic selection method for multi-temporal remote sensing satellite image coverage within a region as described in Implementation Method 1. The method for determining in step 5 whether certain images in the current Ps, excluding Pm, become redundant due to the addition of Pm in step 4 is as follows:

[0049] Step 5.1: Initialize by marking all images in the current Ps as redundant;

[0050] Step 5.2: Determine whether there are any images marked as redundant in the current Ps. If there are, proceed to step 5.3; otherwise, end step 5.

[0051] Step 5.3: Select any image Psi from the currently marked as redundant images, calculate the coverage area Gsi of all images in Ps except Psi. If Gsi can completely contain the coverage area of ​​Psi, then the current Psi is a redundant image and it is removed from Ps. Otherwise, mark it as non-redundant and return to step 5.2.

[0052] Implementation Method 4: This implementation method further defines the automatic selection method for multi-temporal remote sensing satellite image coverage over a large area described in Implementation Method 1. In step 4, the image Pm with the smallest intersection area and non-zero intersection area is selected from the remaining unselected optical remote sensing satellite image set Pr. This selection is based on the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi.

[0053] Implementation Method 5: This implementation method further defines the automatic selection method for multi-temporal remote sensing satellite image coverage over a large area as described in Implementation Method 1. The initialization method in step 1 is as follows: P is the set of all multi-temporal optical remote sensing satellite images, G is the large area range, the selected optical remote sensing satellite image set Ps is an empty set, the coverage range Gs of the selected optical remote sensing satellite images is an empty set, and the remaining unselected optical remote sensing satellite image set Pr is equal to the multi-temporal optical remote sensing satellite image set P.

[0054] The optical remote sensing satellite image set P and the large area range G described in this embodiment are specifically determined by the environment selected by the user.

[0055] Implementation method six: A computer-readable storage medium for storing a computer program that performs the method described in any one of implementation methods one to five.

[0056] Implementation Method Seven: A computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor performs the method according to any one of claims one to five.

[0057] Implementation method eight: A computer program product, which, when executed, implements the method described in implementation method one.

[0058] Implementation Method Nine: This implementation method proposes an example, which is used to explain the above-described implementation methods one through eight. The specific example is as follows:

[0059] See Figures 1 to 6 This embodiment describes an automatic selection method for multi-temporal remote sensing satellite imagery coverage over a large area. The method includes the following steps:

[0060] Algorithm input: initial set of all images P (P should not be empty), set of selected images Ps, set of remaining unselected images Pr, coverage area of ​​selected images Gs, and large area G.

[0061] Step 1, Initialization: Gs = empty, Ps = empty, Pr = P.

[0062] Step 2: Mark all images Pi in the current Pr as "unprocessed".

[0063] Step 3: If there is no image in Pr with a status of "unprocessed", proceed to step 3; otherwise, select any image Pi in Pr with a status of "unprocessed" and determine the positional relationship between Gs and Pi. There are four possible scenarios:

[0064] Case 1: Gs intersects Pi. Calculate the area of ​​the intersection of Gs and Pi, change the status of Pi to "processed", and return to re-execute step 2.

[0065] Case (2): Gs and Pi are separate. Set the area of ​​the intersection of Gs and Pi to 0, change the state of Pi to "processed", and return to execute step 2 again.

[0066] Case (3): Gs contains Pi. Remove Pi from Pr and return to step 2.

[0067] Case (4): If Pi contains Gs, replace Gs with the coverage area of ​​Pi, clear the current Ps, put Pi into Ps, remove Pi from Pr, and return to re-execute step 2.

[0068] Step 4: Select the image Pm in Pr with a non-zero intersection area and the smallest intersection area. If such a Pm does not exist, select any Pi with a zero intersection area as Pm, let Gs = Gs∪Pm's coverage area, add Pm to Ps, and remove Pm from Pr.

[0069] Step 5: For the current Ps, determine whether some images other than Pm in the current Ps have become redundant because Pm was added in the third step.

[0070] Step 6: Determine whether Pr is empty or whether Gs contains G. If so, the algorithm ends; otherwise, return to step 1.

[0071] For step 5, determining whether there are redundant images in the current Ps, the specific steps are as follows:

[0072] Step 5.1: Initialize by marking all images in the current Ps as "redundant".

[0073] Step 5.2: Determine if there are any images marked as "redundant" in the current Ps. If so, proceed to step 5.3; otherwise, end step 4.

[0074] Step 5.3: Select any image Psi from the currently marked as redundant images. Calculate the coverage area Gsi of all images in Ps except Psi. If Gsi completely covers the coverage area of ​​Psi, then the current Psi is a redundant image and is removed from Ps. Otherwise, mark it as "non-redundant". Return to step 5.2.

[0075] This implementation method employs an automatic map selection method based on the coordinates of the four corner points of optical remote sensing satellite imagery and geometric calculations performed on a large area coverage. Figure 3Taking the coverage area of ​​Songyuan City, Jilin Province as an example (the blue coverage area in the figure is the "large area range" mentioned in this patent), its area is 21,184 square kilometers. The "Jilin-1" wide-swath 01B and wide-swath 01C satellites captured a total of 1,382 usable satellite images ("usable satellite images" refers to satellite images filtered according to certain cloud cover, side-swing angle, and image quality; the specific calculation methods for cloud cover and side-swing angle, as well as the image quality evaluation standards, are not within the scope of this patent) in the second quarter of 2023 (the red rectangular area in the figure is the area covered by the coordinates of the four corner points of the satellite images mentioned in this patent). Calculations show that the cumulative area of ​​these 1,382 satellite images is 231,285 square kilometers, covering the area an average of approximately 11 times, and the coverage rate of these 1,382 satellite images for the area is 100%. Manually selecting a subset of satellite images from the 1,382 images is very time-consuming and cannot guarantee that there are no redundant satellite images in the selected subset. There may also be cases where images are missed, resulting in the satellite image coverage of the large area not reaching 100%.

[0076] Using the automatic satellite imagery coverage selection method described in this embodiment, the selection result is as follows: Figure 4 As shown, a total of 226 satellite images were selected. These 226 images still provide 100% coverage of the area, and there are no redundant images.

[0077] The third step of the algorithm described in this embodiment involves selecting a Pm from the remaining unselected optical remote sensing satellite image set Pr. In this method, the selection is based on the positional relationship between Gs and Pi, which is a basic and straightforward image selection approach. In practical applications, the selection rules for Pm can be adjusted according to different needs. The following lists different methods for selecting Pm and their effects.

[0078] (1) Select according to the time of image capture

[0079] When multiple temporal optical remote sensing satellite images exist over a large area, it is often desirable to select the "newest" satellite images, meaning images captured as recently as possible. In this case, the third step of the algorithm can be modified to "select the most recently captured Pi from Pr as Pm, let Gs = Gs∪Pm's coverage area, add Pm to Ps, and remove Pm from Pr." Taking the aforementioned area as an example, using the "select according to the latest captured image" method described in this embodiment, the selection result is as follows... Figure 5 As shown, a total of 229 satellite images were selected. These 229 images still provide 100% coverage of the area, and there are no redundant images.

[0080] (2) Select based on the largest coverage area in a single shot

[0081] When producing a large-area single map, it is often desirable to select satellite imagery from the same or a few consecutive captures. Large-area single maps produced from such imagery typically exhibit better geometric positioning and apparent radiometric properties. During initialization, the algorithm groups all images Pi in P according to their capture time. For each group, its coverage area is calculated. Each time, the group with the largest intersection area with the currently uncovered area is selected. The original method can be adjusted as follows:

[0082] Method input: initial set of all images P (P should not be empty), selected set of images Ps, currently uncovered area of ​​the large region Gu, and large region range G.

[0083] Initialization: Gu = G, Ps = empty, P is divided into multiple groups Pt according to the shooting time.

[0084] The steps are as follows:

[0085] Step 1: Mark all image groups Pti in the current Pt as "Unprocessed".

[0086] Step 2: If there are no image groups in Pt with a status of "unprocessed", proceed to Step 3; otherwise, randomly select an image group Pti with a status of "unprocessed" in Pt and determine the positional relationship between Gu and Pti. There are two possible scenarios:

[0087] Case 1: Gu intersects with Pti. Calculate the area of ​​the intersection of Gu and Pti, change the status of Pti to "processed", and return to re-execute step 2.

[0088] Case (2): Gu is separated from Pti. Remove the group Pti from Pt and return to step 2.

[0089] Step 3: Select the image group Pti with the largest intersection area in Pt. Calculate whether Pi and Gu intersect in each image group of Pti. Add the intersecting Pi to Ps and discard the disjoint Pi. Let Gu = Gu - the coverage area of ​​Pti.

[0090] Step 4: Check if Pt is empty. If it is, the algorithm ends. Otherwise, check if Gu is empty. If it is, the algorithm ends. Otherwise, return to step 2.

[0091] Taking the aforementioned area as an example, using the "select based on the largest coverage area in a single shot" method described in this embodiment, the selection result is as follows: Figure 6 As shown, a total of 197 satellite images were selected. These 197 images still provide 100% coverage of the area, and there are no redundant images.

[0092] In summary, the method described in this embodiment, which performs geometric calculations based on the coordinates of the four corner points and the coverage area of ​​a large area of ​​optical remote sensing satellite images, automatically selects a subset of optical remote sensing satellite images from these multi-temporal-captured images that can guarantee the maximum coverage of the large area without redundant coverage.

[0093] Figure 1 Any process or method described in the flowcharts or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a custom logical function or process. The scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved, as will be understood by those skilled in the art to which embodiments of the invention pertain. The logic and / or steps shown in the flowcharts or otherwise described herein illustrate the architecture, functionality, and operation of possible implementations of apparatuses and methods according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the figures. For example, two consecutively shown blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. For example, a sequence of executable instructions that can be considered as implementing logical functions can be embodied in any computer-readable medium for use by instruction execution systems, apparatus, or devices (such as computer-based systems, processor-based systems, or other systems that can fetch and execute instructions from instruction execution systems, apparatus, or devices), or in conjunction with such instruction execution systems, apparatus, or devices.

[0094] For the purposes of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection (electronic device) having one or N wires, a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, a computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory. It should be understood that various parts of the invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0095] Those skilled in the art will understand that the above description is merely a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. This is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0096] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for automatically selecting coverage of multi-temporal remote sensing satellite imagery over a large area, characterized in that, The method includes the following steps: Step 1: Initialize the set P of all multi-temporal optical remote sensing satellite images, the selected optical remote sensing satellite images Ps, the set Pr of the remaining unselected optical remote sensing satellite images, the coverage area Gs of the selected optical remote sensing satellite images, and the large area G. Step 2: Mark all remaining unselected optical remote sensing satellite images in the Pr set as unprocessed; Step 3: If there is no image in the remaining unselected optical remote sensing satellite image set Pr that is in an unprocessed state, then proceed to step 4. Otherwise, select any image Pi in the remaining unselected optical remote sensing satellite image set Pr that is in an unprocessed state, calculate the intersection area of ​​Gs and Pi based on the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi, and change the state of Pi to processed. Step 4: Select the image Pm with a non-zero intersection area and the smallest intersection area from the remaining unselected optical remote sensing satellite image set Pr. If there is no image Pm with a non-zero intersection area and the smallest intersection area, then select any Pi with a zero intersection area as Pm, let Gs = Gs∪Pm's coverage area, and add the image Pm with a non-zero intersection area and the smallest intersection area to the selected optical remote sensing satellite image Ps. Remove Pm from the remaining unselected optical remote sensing satellite image set Pr. Step 5: For the selected optical remote sensing satellite image Ps, determine whether some images other than Pm in the current Ps become redundant due to the addition of Pm in step 4. Step 6: Determine whether the remaining unselected optical remote sensing satellite image set Pr is empty or whether the coverage area Gs of the selected optical remote sensing satellite image contains a large area G. If so, output the currently selected optical remote sensing satellite image set Ps, that is, the subset of optical remote sensing satellite images with the largest coverage and no redundant coverage. Otherwise, return to step 2. In step 3, the intersection area of ​​Gs and Pi is calculated based on the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi. This includes: When Gs intersects Pi, calculate the area of ​​the intersection of Gs and Pi, change the state of Pi to processed, and return to re-execute step 3; When Gs and Pi are separated, set the area of ​​the intersection of Gs and Pi to 0, change the state of Pi to processed, and return to re-execute step 3; When Gs contains Pi, remove Pi from Pr and return to step 3 again; When P i If Gs is included, replace Gs with the coverage area of ​​Pi, clear the current Ps, put Pi into Ps, remove Pi from Pr, and return to re-execute step 3. The method for determining in step 5 whether certain images in the current Ps, excluding Pm, become redundant due to the addition of Pm in step 4 is as follows: Step 5.1: Initialize by marking all images in the current Ps as redundant; Step 5.2: Determine whether there are any images marked as redundant in the current Ps. If there are, proceed to step 5.3; otherwise, end step 5. Step 5.3: Select any image Psi from the currently marked as redundant images, calculate the coverage area Gsi of all images in Ps except Psi. If Gsi can completely contain the coverage area of ​​Psi, then the current Psi is a redundant image and it is removed from Ps. Otherwise, mark it as non-redundant and return to step 5.

2.

2. The method for automatic selection of multi-temporal remote sensing satellite imagery coverage over a large area according to claim 1, characterized in that, In step 4, the image Pm with the smallest intersection area and non-zero intersection area is selected from the remaining unselected optical remote sensing satellite image set Pr. This selection is based on the positional relationship between the coverage area Gs of the selected optical remote sensing satellite image and the unprocessed image Pi.

3. The method for automatic selection of multi-temporal remote sensing satellite imagery coverage over a large area according to claim 1, characterized in that, The initialization method in step 1 is as follows: P is the set of all optical remote sensing satellite images acquired in multiple time phases, G is the large area range, the selected optical remote sensing satellite image set Ps is an empty set, the coverage area Gs of the selected optical remote sensing satellite images is an empty set, and the remaining unselected optical remote sensing satellite image set Pr is equal to the set of optical remote sensing satellite images acquired in multiple time phases, then initialization is performed.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that performs the method according to any one of claims 1 to 3.

5. A computer device, including a memory and a processor, characterized in that, The memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor performs the method according to any one of claims 1 to 3.

6. A computer program product, as a computer program, is characterized by: When the computer program is executed, it implements the method of claim 1.

Images