Method for automatically selecting multi-temporal remote sensing satellite image coverage in large area

Abstract

The invention discloses an automatic selection method for multi-temporal remote sensing satellite image coverage in a large area, and relates to the field of optical remote sensing satellite image application. The problems that in the prior art, the efficiency of selecting a satellite image used for final embedding is low, it cannot be guaranteed that no redundant satellite image exists in a selected satellite image subset, and the coverage rate of a large-area satellite image is not maximum due to the fact that selection missing often exists are solved. Through a selection method for carrying out geometric operation based on coordinates of four corners of an optical remote sensing satellite image and a large-area coverage range, 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 an optical remote sensing satellite image set shot in multiple time phases. The method is also suitable for the application field of selecting the 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] The present invention relates to the technical field of optical remote sensing satellite image application, and in particular to a method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area. Background Art

[0002] With the launch and operation of a large number of optical remote sensing satellites in orbit in recent years, the acquisition capability of optical remote sensing satellite images has been significantly improved. When mosaicking satellite images of a large area, there are often satellite images taken in multiple phases in the area, and these satellite images cover more than one layer of the large area. However, in the actual work process, the mosaicking of satellite images of a large area only requires non-redundant images that can cover one layer of the large area, so it is necessary to select a subset of satellite images that can guarantee the maximum coverage rate and non-redundant coverage for the large area from the multi-phase and multi-layer remote sensing satellite image collection. Traditional satellite image selection mainly relies on manual screening of each image sheet, and often requires selecting satellite images for the final mosaicking from a satellite image collection that is several times the number of actual selection results. The above process is very time-consuming, and there is no guarantee that there are no redundant satellite images in the selected satellite image subset. There are often omissions, which leads to the satellite image coverage rate of the large area not reaching the maximum. Summary of the invention

[0003] The present invention aims at the problem that the satellite image selection work in the prior art mainly relies on the manual screening of each image sheet, and often needs to select the satellite images used for the final mosaic from a satellite image set that is several times the number of the actual selection results. The above process is very time-consuming, and it cannot guarantee that there are no redundant satellite images in the selected satellite image subset. There are often omissions, resulting in the problem that the satellite image coverage rate of a large area does not reach the maximum.

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

[0005] Solution 1: The present invention proposes a method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area, the method comprising the following steps:

[0006] Step 1, initialize all optical remote sensing satellite image sets P taken in multiple temporal phases, selected optical remote sensing satellite images Ps, remaining unselected optical remote sensing satellite image sets Pr, coverage range Gs of selected optical remote sensing satellite images, and large area range G;

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

[0008] Step 3: If there is no unprocessed image in the remaining unselected optical remote sensing satellite image set Pr, execute step 4; otherwise, select any unprocessed image Pi in the remaining unselected optical remote sensing satellite image set Pr, calculate the intersection area of ​​Gs and Pi according to the positional relationship between the coverage range 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 an 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, select any Pi with a zero intersection area as Pm, set Gs=Gs∪Pm, 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, and remove Pm from the remaining unselected optical remote sensing satellite image set Pr.

[0010] Step 5: for the optical remote sensing satellite image Ps that has been selected at this time, determine whether some images other than Pm in the current Ps become redundant due to the newly added 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 range Gs of the selected optical remote sensing satellite image contains the large area range 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 maximum coverage and no redundant coverage. Otherwise, return to step 2.

[0012] Further, a preferred implementation manner is provided, wherein in step 3, the intersection area of ​​Gs and Pi is calculated based on the positional relationship between the coverage range Gs of the selected optical remote sensing satellite image and the unprocessed image Pi, and 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, the area of ​​the intersection of Gs and Pi is set to 0, the state of Pi is changed to processed, and step 3 is executed again;

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

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

[0017] Further, a preferred implementation is provided, in step 5, the method for determining whether some images other than Pm in the current Ps become redundant due to the newly added Pm in step 4 is:

[0018] Step 5.1, initialization, mark all images in the current Ps as redundant;

[0019] Step 5.2: Determine whether there is an image marked as redundant in the current Ps. If so, execute step 5.3; otherwise, end step 5.

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

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

[0022] Furthermore, a preferred implementation is provided, in which the initialization method in step 1 is: P is the set of all optical remote sensing satellite images shot in multiple temporal phases, G is a large area, 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 optical remote sensing satellite image set P shot in multiple temporal phases for initialization.

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

[0024] Solution three: A computer device includes 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 any one of the methods described in Solution one.

[0025] Solution 4: A computer program product, as a computer program, implements the method described in Solution 1 when the computer program is executed.

[0026] The present invention is beneficial in that:

[0027] The present invention proposes a method for performing geometric operations based on the coordinates of the four corner points of optical remote sensing satellite images and the coverage of a large area, which automatically selects a subset of optical remote sensing satellite images that can guarantee the maximum coverage rate and non-redundant coverage for the large area from a set of optical remote sensing satellite images taken in multiple phases.

[0028] The present invention is also applicable to the application field of selecting an optical remote sensing satellite image subset that can ensure maximum coverage and non-redundant coverage for the large area. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of a process flow of a method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area as described in Implementation Method 1.

[0030] Figure 2 Schematic diagram of the redundant image determination process described in Implementation Method 1.

[0031] Figure 3 This is a schematic diagram of the initial query results described in Implementation Method 9.

[0032] Figure 4 This is a diagram showing the result of using the coverage optimization algorithm described in Implementation Mode 9.

[0033] Figure 5 This is a schematic diagram of the optimization result using the latest coverage according to the shooting time as described in the ninth implementation mode.

[0034] Figure 6 This is a schematic diagram of the results of the ninth implementation mode after the maximum optimization of the coverage area of ​​a single shot. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the implementation methods of the present application clearer, the technical solutions in the implementation methods of the present application will be clearly and completely described below in conjunction with the drawings in the implementation methods of the present application. Obviously, the described implementation methods are only part of the implementation methods of the present application, not all of the implementation methods.

[0036] Embodiment 1: This embodiment provides a method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area, and the method includes the following steps:

[0037] Step 1, initialize all optical remote sensing satellite image sets P taken in multiple temporal phases, selected optical remote sensing satellite images Ps, remaining unselected optical remote sensing satellite image sets Pr, coverage range Gs of selected optical remote sensing satellite images, and large area range G;

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

[0039] Step 3: If there is no unprocessed image in the remaining unselected optical remote sensing satellite image set Pr, execute step 4; otherwise, select any unprocessed image Pi in the remaining unselected optical remote sensing satellite image set Pr, calculate the intersection area of ​​Gs and Pi according to the positional relationship between the coverage range 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 an 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, select any Pi with a zero intersection area as Pm, set Gs=Gs∪Pm, 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, and remove Pm from the remaining unselected optical remote sensing satellite image set Pr.

[0041] Step 5: for the optical remote sensing satellite image Ps that has been selected at this time, determine whether some images other than Pm in the current Ps become redundant due to the newly added 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 range Gs of the selected optical remote sensing satellite image contains the large area range 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 maximum coverage and no redundant coverage. Otherwise, return to step 2.

[0043] Embodiment 2: This embodiment further limits the method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area described in embodiment 1. In step 3, the intersection area of ​​Gs and Pi is calculated based on the positional relationship between the coverage range Gs of the selected optical remote sensing satellite image and the unprocessed image Pi, including:

[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, the area of ​​the intersection of Gs and Pi is set to 0, the state of Pi is changed to processed, and step 3 is executed again;

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

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

[0048] Implementation method 3: This implementation method further limits the method for automatically selecting multi-temporal remote sensing satellite image coverage in the region described in implementation method 1. In step 5, the method for determining whether some images other than Pm in the current Ps become redundant due to the newly added Pm in step 4 is as follows:

[0049] Step 5.1, initialization, mark all images in the current Ps as redundant;

[0050] Step 5.2: Determine whether there is an image marked as redundant in the current Ps. If so, execute step 5.3; otherwise, end step 5.

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

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

[0053] Implementation method five. This implementation method is a further limitation of the method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area described in implementation method one. The initialization method in step 1 is: P is a set of all optical remote sensing satellite images taken in multiple temporal phases, G is a large area, 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 optical remote sensing satellite image set P taken in multiple temporal phases for initialization.

[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] Embodiment 6: A computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program executes the method described in any one of embodiments 1 to 5.

[0056] Embodiment 7: A computer device comprises 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 according to any one of claims 1 to 5.

[0057] Embodiment 8: A computer program product, as a computer program, implements the method described in embodiment 1 when the computer program is executed.

[0058] Embodiment 9: This embodiment provides an example, which is used to explain the above embodiments 1 to 8. The specific example is as follows:

[0059] See also Figures 1 to 6 To illustrate this implementation, this embodiment proposes a method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area, and the method comprises the following steps:

[0060] Algorithm input: the initial set of all images P (which should not be an empty set), the selected image set Ps, the remaining unselected image set Pr, the selected image coverage Gs, and the large area range 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 the status of "unprocessed", then execute the third step. Otherwise, select any image Pi in Pr with the status of "unprocessed" and judge the positional relationship between Gs and Pi. There are four situations:

[0064] Case (I): Gs intersects with 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 separated, the area of ​​the intersection of Gs and Pi is set to 0, the state of Pi is changed to "processed", and the second step is executed again;

[0066] Case (III): Gs contains Pi, remove Pi from Pr, and return to execute step 2 again;

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

[0068] Step 4: Select the image Pm with the smallest intersection area and non-zero intersection area in Pr. If such a Pm does not exist, select any Pi with zero intersection area as Pm, set Gs=Gs∪Pm’s coverage, 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 become redundant because Pm is newly 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 the first step.

[0071] For step 5, it is determined whether there is a redundant image in the current image Ps. The specific steps are as follows:

[0072] Step 5.1: Initialize and mark all images in the current Ps as “redundant”.

[0073] Step 5.2: Determine whether there is an image marked as "redundant" in the current Ps. If so, execute step 5.3, otherwise, the fourth step ends.

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

[0075] This embodiment adopts an automatic image selection method based on the coordinates of the four corner points of optical remote sensing satellite images and the large area coverage for geometric calculations. 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" described in this patent), its area is 21,184 square kilometers. In the second quarter of 2023, the wide-band 01B satellite and the wide-band 01C satellite of "Jilin-1" took a total of 1,382 scenes of available satellite images ("available satellite images" refer to satellite images that have been screened according to certain cloud cover, side swing angle, and image quality. The specific cloud cover and side swing angle calculation methods and image quality evaluation standards are not within the scope of this patent). The red rectangular area in the figure is the range covered by the coordinates of the four corner points of the satellite image described in this patent. After calculation, the cumulative area of ​​the 1,382 satellite images is 231,285 square kilometers, covering the area about 11 times on average, and the coverage rate of the 1,382 satellite images for the area is 100%. It is very time-consuming to select an image subset from the 1,382 satellite images by manual selection, and there is no guarantee that there are no redundant satellite images in the selected satellite image subset. There may also be omissions, resulting in the satellite image coverage of the large area not reaching 100%.

[0076] The automatic selection method for satellite image coverage described in this embodiment is used, and the selection result is as follows: Figure 4 A total of 226 satellite images were selected, and the coverage rate of the 226 satellite images for the area is still 100%, and there is no redundant coverage image.

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

[0078] (1) Select by image shooting time

[0079] When there are optical remote sensing satellite images taken in multiple phases in a large area, it is often desired that the selected satellite images are as "new" as possible, that is, the closer the shooting time of the selected satellite images is to the current time, the better. In this case, the third step of the algorithm can be changed to "select Pi with the latest shooting time in Pr as Pm, let Gs = Gs ∪ Pm's coverage, and add Pm to Ps, and remove Pm from Pr". Still taking the above area as an example, using the method of "selecting according to the latest image shooting time" described in this embodiment, the selection result is as follows: Figure 5 A total of 229 satellite images were selected, and the coverage rate of the 229 satellite images for the area is still 100%, and there is no redundant coverage image.

[0080] (2) Select the maximum coverage area in a single shot

[0081] When producing a large area map, it is often hoped that the selected satellite images are taken at the same time or as few times as possible. In this way, the large area map produced by the selected satellite images often has better results in geometric positioning and apparent radiation. When the algorithm is initialized, all images Pi in P are grouped according to the shooting time, and the coverage area of ​​each group of images is calculated. Each time, the group of images with the largest intersection area with the current uncovered area is selected. The original method process can be adjusted as follows:

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

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

[0084] Here are the steps:

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

[0086] Step 2: If there is no image group with the status of "unprocessed" in Pt, then execute step 3. Otherwise, select any image group Pti with the status of "unprocessed" in Pt and determine the positional relationship between Gu and Pti. There are two situations:

[0087] Case (I): 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 the second step;

[0088] Case (2): Gu is separated from Pti, the group Pti is removed from Pt, and the process returns to execute the second step again;

[0089] Step 3: Select the image group Pti with the largest intersection area in Pt, calculate whether Pi in Pti intersects with Gu one by one, add the intersecting Pi to Ps, and discard the separated Pi. Let Gu = Gu-Pti coverage.

[0090] Step 4: Determine whether Pt is empty. If so, the algorithm ends. Otherwise, continue to determine whether Gu is equal to empty. If so, the algorithm ends. Otherwise, return to step 2.

[0091] Still taking the above area as an example, using the method of "selecting by the largest coverage area of ​​a single shot" described in this embodiment, the selection result is as follows: Figure 6 A total of 197 satellite images were selected, and the coverage rate of these 197 satellite images for the area is still 100%, and there is no redundant coverage image.

[0092] To summarize, the method described in this embodiment for performing geometric operations based on the coordinates of the four corner points of optical remote sensing satellite images and the coverage range of a large area automatically selects a subset of optical remote sensing satellite images from a set of optical remote sensing satellite images taken in multiple phases, which can ensure maximum coverage and no redundant coverage for the large area.

[0093] Figure 1 Any process or method description in the flowchart described in or otherwise described herein can be understood as representing a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a custom logic function or process, and the scope of the preferred embodiment of the present invention includes other implementations, in which the functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by a person skilled in the art of the present invention. The logic and / or steps represented in the flowchart or otherwise described herein illustrate the possible implementation architecture, functions and operations of the apparatus and methods according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It is also noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions. For example, what may be considered as an ordered list of executable instructions for implementing a logical function may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions in an instruction execution system, apparatus, or device).

[0094] For the purposes of the present specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or equipment or in conjunction with these instruction execution systems, devices or equipment. More specific examples of computer-readable media (non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program may be printed, because the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting or processing in other suitable ways as necessary, and then stored in a computer memory. It should be understood that the various parts of the present invention may be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a 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 one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0095] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0096] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area, characterized in that: The method comprises the following steps: Step 1, initialize all optical remote sensing satellite image sets P taken in multiple temporal phases, selected optical remote sensing satellite images Ps, remaining unselected optical remote sensing satellite image sets Pr, coverage range Gs of selected optical remote sensing satellite images, and large area range G; Step 2: Mark all the optical remote sensing satellite images in the remaining unselected optical remote sensing satellite image set Pr as unprocessed; Step 3: If there is no unprocessed image in the remaining unselected optical remote sensing satellite image set Pr, execute step 4; otherwise, select any unprocessed image Pi in the remaining unselected optical remote sensing satellite image set Pr, calculate the intersection area of ​​Gs and Pi according to the positional relationship between the coverage range 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 an 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, select any Pi with a zero intersection area as Pm, set Gs=Gs∪Pm, 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, and remove Pm from the remaining unselected optical remote sensing satellite image set Pr. Step 5: for the optical remote sensing satellite image Ps that has been selected at this time, determine whether some images other than Pm in the current Ps become redundant due to the newly added Pm in step 4; Step 6: Determine whether the remaining unselected optical remote sensing satellite image set Pr is empty or whether the coverage range Gs of the selected optical remote sensing satellite image contains the large area range 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 maximum coverage and no redundant coverage. Otherwise, return to step 2.

2. The method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area according to claim 1, characterized in that: In step 3, the intersection area of ​​Gs and Pi is calculated based on the positional relationship between the coverage range Gs of the selected optical remote sensing satellite image and the unprocessed image Pi, including: 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, the area of ​​the intersection of Gs and Pi is set to 0, the state of Pi is changed to processed, and step 3 is executed again; When Gs contains Pi, remove Pi from Pr and return to re-execute step 3; When P i If Gs is included, replace Gs with the coverage range of Pi, clear the current Ps and put Pi into Ps, remove Pi from Pr, and return to re-execute step 3.

3. The method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area according to claim 1, characterized in that: In step 5, the method for determining whether some images other than Pm in the current Ps become redundant due to the newly added Pm in step 4 is as follows: Step 5.1, initialization, mark all images in the current Ps as redundant; Step 5.2: Determine whether there is an image marked as redundant in the current Ps. If so, execute step 5.3; otherwise, end step 5. Step 5.3: Select any image Psi from the images currently marked as redundant, and calculate the coverage Gsi of all images in Ps except Psi. If Gsi can completely contain the coverage of Psi, then the current Psi is a redundant image and it is removed from Ps. Otherwise, it is marked as non-redundant and returns to step 5.

2.

4. The method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area according to claim 1, characterized in that: In step 4, the image Pm with a non-zero intersection area and a minimum intersection area is selected from the remaining unselected optical remote sensing satellite image set Pr, which is obtained by selecting the positional relationship between the coverage range Gs of the selected optical remote sensing satellite image and the unprocessed image Pi.

5. The method for automatically selecting multi-temporal remote sensing satellite image coverage in a large area according to claim 1, characterized in that: The initialization method in step 1 is: P is the set of all optical remote sensing satellite images taken in multiple temporal phases, 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 optical remote sensing satellite image set P taken in multiple temporal phases for initialization.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store a computer program, and the computer program executes the method according to any one of claims 1 to 5.

7. A computer device comprising a memory and a processor, characterized in that A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor implements the method according to any one of claims 1 to 5.

8. A computer program product, being a computer program, characterized in that When the computer program is executed, the method of claim 1 is implemented.

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