Multi-satellite regional mosaic imaging task planning method and computer device

Abstract

The application relates to the technical field of satellite imaging, in particular to a multi-satellite regional splicing imaging task planning method and computer equipment. The method provided by the application comprises the following steps: dividing a target region to obtain at least one target region sub-block; for each target region sub-block, acquiring an available imaging window of a candidate satellite relative to the target region sub-block; assigning a corresponding candidate satellite and available imaging window to each target region sub-block to obtain a preliminary task planning scheme; for each group of adjacent target region sub-blocks, determining an actual overlap condition according to the preliminary task planning scheme, verifying the actual overlap condition based on a sub-block overlap rule, and generating a final task planning scheme. The method provided by the application solves the technical problem of reasonably planning a multi-line cooperative imaging task, and improves the final satellite imaging quality.

Description

Technical Field

[0001] This application relates to the field of satellite imaging technology, and in particular to a multi-satellite regional stitching imaging mission planning method and computer equipment. Background Technology

[0002] With the rapid development of Earth observation satellite technology, the imaging swath width of a single satellite is no longer sufficient to meet the high-resolution imaging requirements of large areas. For example, for a wide-area mapping mission of 200km × 200km, even with side-swing maneuvers, a single satellite cannot complete full-coverage imaging in a single pass. To solve this problem, a multi-satellite collaborative approach is usually adopted, where multiple satellites image blocks simultaneously, and then the images from each block are stitched together to form a complete regional image.

[0003] However, traditional methods for planning region-based imaging missions currently suffer from several problems. First, traditional methods use simple grid partitioning, often resulting in unreasonable sizes and numbers of imaging sub-blocks, affecting imaging efficiency. Second, in multi-satellite collaboration, the imaging capabilities of different satellites vary, and overly simplistic allocation logic can impact image quality. Finally, because different satellites image at different times and in different attitudes, the acquired images differ in geometric position, radiometric characteristics, and other aspects. In particular, the edge regions of adjacent sub-blocks are highly susceptible to the influence of factors such as the side-swing angle, orbit, and attitude of the imaging satellites, affecting imaging parameters such as the actual imaging range and consequently the quality of the stitched image.

[0004] Therefore, how to rationally plan multi-line collaborative imaging tasks is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a multi-satellite regional mosaic imaging mission planning method and computer equipment, which solves the technical problem of rationally planning multi-line collaborative imaging missions and improves the final satellite imaging quality.

[0006] To achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, embodiments of this application provide a multi-satellite region stitching imaging task planning method, the method comprising: The target region is divided to obtain at least one target region sub-block, wherein the division is such that the size of each target region sub-block matches the imaging swath width of the candidate satellite, and the overlapping area between adjacent target region sub-blocks satisfies a preset sub-block overlap rule. For each target region sub-block, obtain the available imaging window of the candidate satellite relative to the target region sub-block. The available imaging window is the time interval during which the payload's field of view can cover the target region sub-block during the candidate satellite's transit. For each target region sub-block, a corresponding candidate satellite and available imaging window are assigned to obtain a preliminary mission planning scheme. The assignment is based on the imaging quality of the available imaging window. For each group of adjacent target area sub-blocks, the actual overlap is determined based on the preliminary task planning scheme, and the actual overlap is verified based on the sub-block overlap rules to generate the final task planning scheme.

[0007] The multi-satellite regional stitching imaging mission planning method proposed in this application solves the problem of unreasonable sub-block settings caused by simple grid division logic by dividing the target area into sub-blocks that match the imaging swath width of the satellites and introducing sub-block overlap rules. By obtaining the available imaging windows of candidate satellites for each sub-block and allocating them based on imaging quality, it overcomes the defect of simple satellite allocation logic affecting imaging quality in traditional methods. Furthermore, by verifying the actual overlap of adjacent sub-blocks in the planning scheme, it ensures that they meet the preset overlap rules, thereby effectively dealing with the deviation of the actual imaging range caused by factors such as the side angle, orbit or attitude changes of the imaging satellites, avoiding stitching failure or affecting image quality due to insufficient overlap, and improving the efficiency and reliability of multi-satellite collaborative imaging stitching.

[0008] Secondly, embodiments of this application provide a computer device, including: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to perform the multi-satellite region stitching imaging task planning method described in the first aspect. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 A step diagram illustrating a multi-satellite region stitching imaging task planning method provided in this application embodiment; Figure 2 A schematic diagram illustrating a specific target region division method provided in an embodiment of this application; Figure 3 A flowchart illustrating the implementation of a specific multi-satellite region stitching imaging task planning method provided in this application embodiment; Figure 4 A schematic diagram of a multi-satellite region stitching imaging task planning device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0011] 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 some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] This application provides a method for planning a multi-satellite region stitching imaging task. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0013] The first embodiment of this application provides a multi-satellite region stitching imaging task planning method, such as... Figure 1 As shown, it includes the following steps: Step 110: Divide the target region to obtain at least one target region sub-block, wherein the division is such that the size of each target region sub-block matches the imaging swath of the candidate satellite, and the overlapping area between adjacent target region sub-blocks satisfies the preset sub-block overlap rule.

[0014] In this embodiment, the designated large target area first needs to be reasonably divided into several sub-blocks so that different satellites can image and stitch them together later.

[0015] This embodiment proposes that one rule for partitioning is to adapt the size of each sub-block to the imaging capability of the candidate satellite, thereby ensuring that the satellite can complete the imaging of the corresponding sub-block in a single pass.

[0016] Another rule for segmentation is to reserve a certain overlap area between adjacent sub-blocks. Specifically, according to the preset sub-block overlap rules, an overlap area is reserved in each target region sub-block to meet the requirements of geometric continuity between adjacent sub-blocks for image stitching and improve stitching quality.

[0017] In one embodiment, the sub-block overlap rule can preset a reasonable range for the area of ​​the overlapping region, requiring that its area be greater than the minimum threshold required to ensure successful splicing, and less than the maximum threshold to avoid data redundancy and waste, thereby achieving a balance between splicing quality and resource utilization efficiency.

[0018] Step 120: For each target region sub-block, obtain the available imaging window of the candidate satellite relative to the target region sub-block. The available imaging window is the time interval during which the payload's field of view can cover the target region sub-block during the candidate satellite's transit.

[0019] After the target area is divided, the relationship between each sub-block and the candidate satellite is established.

[0020] Specifically, for each candidate satellite, it is necessary to combine its orbital parameters, attitude, and imaging parameters to determine the time window during which its image acquisition range can completely cover a certain sub-block, i.e., the usable imaging window.

[0021] The available imaging window reflects whether the satellite has the physical conditions to perform an imaging mission on that sub-block. For example, the flight trajectory of a candidate satellite can be extrapolated from orbital prediction data, and the yaw capability and rotation capability of the candidate satellite's image acquisition equipment can be combined to determine whether its field of view can completely cover a certain sub-block during the mission planning period.

[0022] Step 130: Assign corresponding candidate satellites and available imaging windows to each target area sub-block to obtain a preliminary mission planning scheme. The assignment is based on the imaging quality of the available imaging windows.

[0023] After obtaining the available imaging windows for all sub-blocks, the imaging quality of each available imaging window for the sub-block is evaluated. The optimal window is selected for each sub-block, and the corresponding candidate satellites are recorded to form a preliminary task allocation scheme.

[0024] Specifically, imaging quality can be comprehensively considered by taking into account the impact of factors such as lighting conditions, resolution, and atmospheric interference on the final image effect. For example, based on the geographical features of sub-blocks and the timeliness requirements of the mission, multiple imaging windows available at different times can be compared, and those windows with better imaging geometry and less susceptibility to environmental influences can be prioritized, thereby improving the overall imaging consistency and availability under conditions of limited multi-satellite resources.

[0025] Step 140: For each group of adjacent target area sub-blocks, determine the actual overlap based on the preliminary task planning scheme, verify the actual overlap based on the sub-block overlap rules, and generate the final task planning scheme.

[0026] In practical applications, factors related to satellite imaging, such as side sway angle and orbital deviation, can affect the actual imaging range. The theoretical overlap of adjacent sub-blocks in the preliminary scheme may not be achieved in actual imaging.

[0027] Therefore, this embodiment proposes to determine the true range of the candidate satellite's imaging of the target area sub-blocks based on the preliminary allocation results, thereby determining the actual overlap between adjacent target area sub-blocks, and comparing it again with the sub-block overlap rules to determine the final mission planning scheme.

[0028] In one embodiment, the verification process is as follows: if the actual overlap of all adjacent sub-blocks meets the requirements, then the preliminary solution is the final solution; otherwise, the adjacent sub-blocks that do not meet the conditions need to be adjusted until the overlap is compliant, thereby ensuring the feasibility and accuracy of subsequent image stitching.

[0029] The second embodiment of this application further specifies the multi-star region stitching imaging task planning method in more detail and in more specific terms than the first embodiment. Some or all of the technical features in the second embodiment can be combined with or replaced by the first embodiment, either individually or in combination, to obtain more feasible multi-star region stitching imaging task planning methods.

[0030] The multi-star region stitching imaging task planning method in the second embodiment of this application is described in detail below: Optionally, for each group of adjacent target region sub-blocks, the actual overlap is determined according to the preliminary mission planning scheme, including: for each group of adjacent target region sub-blocks, the actual imaging range of the candidate satellite on the adjacent target region sub-blocks is determined according to the actual imaging parameters of at least one candidate satellite allocated to the target region sub-block in the preliminary mission planning scheme; and the actual overlap rate of the adjacent target region sub-blocks is determined according to the actual imaging range.

[0031] In the initial allocation scheme, adjacent target region sub-blocks may be imaged by different satellites at different side-swing angles, resulting in a deviation between the actual imaging coverage and the theoretical division, affecting the overlap. This embodiment proposes defining the actual overlap rate of adjacent target region sub-blocks to quantitatively characterize the actual overlap.

[0032] Specifically, the actual overlap rate is defined as the ratio of the area / side length of the overlapping portion of adjacent target region sub-blocks to the area / side length of the target region sub-block.

[0033] For example, for two adjacent sub-blocks in the horizontal direction and Based on the actual imaging parameters of the assigned candidate satellites, such as the side-swing angle and imaging time, the actual ground coverage area is calculated. Let sub-blocks... The longitude of the right boundary of the image is , sub-block The longitude of the left boundary of the image is The width of the overlapping area for: Depending on actual needs, longitude can be converted into actual distance.

[0034] Set sub-block and They are the same size and have the same side length. The actual overlap rate for: This embodiment introduces the actual overlap rate quantification to assess the actual overlap of adjacent target area sub-blocks, providing an accurate evaluation of the overlap between adjacent sub-blocks. This accurately reflects the deviation of the actual coverage of different satellites under different imaging conditions, providing an objective and reliable basis for subsequent overlap verification.

[0035] Optionally, the actual overlap is verified based on the sub-block overlap rule to generate the final mission planning scheme, including: for each group of adjacent target area sub-blocks whose actual overlap rate does not meet the sub-block overlap rule, a preset adjustment operation is performed and the actual overlap rate is recalculated until the recalculated actual overlap rate meets the preset overlap rule. The preset adjustment operation includes at least one of the following: adjusting the actual imaging parameters of the candidate satellite, adjusting the boundary of the target area sub-block, and replacing the candidate satellite; the mission planning scheme in which the actual overlap rates of adjacent target area sub-blocks all meet the sub-block overlap rule is taken as the final mission planning scheme.

[0036] For adjacent target area sub-blocks whose actual overlap rate does not meet the sub-block overlap rules, this embodiment proposes to perform a preset adjustment operation and recalculate the actual overlap rate.

[0037] Specifically, taking the actual overlap rate as an example, the sub-block overlap rule is a percentage range. .

[0038] In one embodiment, sub-block overlap rules It is 10% to 15%. and Compare: like If so, then the sub-block overlap rule is satisfied; like If the sub-block overlap rule is not met, it may result in splicing gaps. like If the sub-block overlap rule is not met, imaging efficiency will be wasted.

[0039] When the sub-block overlap rule is not met, an adjustment mechanism is triggered. This embodiment provides three optional adjustment strategies: adjusting the actual imaging parameters of the candidate satellite, adjusting the boundaries of the target region sub-block, and replacing the candidate satellite.

[0040] In one embodiment, three strategies are assigned priorities, and the adjustment strategies are executed sequentially according to their priorities: First priority: Adjusting actual imaging parameters For sub-blocks with overlapping ratio deviations, attempt to fine-tune the imaging parameters of their assigned satellites, specifically by adjusting the side-swing angle. Adjusting the side-swing angle will change the offset of the imaging coverage area, thus affecting the actual boundary position.

[0041] In one embodiment, the adjustment step size is set to 0.1°, and the maximum adjustment range does not exceed 10% of the satellite's maximum side yaw angle range. After adjustment, the overlap rate is recalculated until the requirements are met.

[0042] Second priority: Adjust the boundaries of the target region sub-blocks If adjusting the imaging parameters fails to meet the requirements, adjust the sub-block boundaries. Shift the sub-block boundaries towards the direction of insufficient overlap.

[0043] In one embodiment, the translation step size is set to 1% of the sub-block width, and the maximum translation range does not exceed 5% of the sub-block width. After adjustment, it must be ensured that all sub-blocks can still completely cover the target area, and the overlap rate is recalculated.

[0044] Third priority: Replace candidate satellites If the above adjustments fail to meet the requirements, a satellite replacement mechanism will be triggered to reselect satellites with suboptimal scores for the relevant sub-blocks and recalculate the overlap rate.

[0045] In one embodiment, for a sub-block where candidate satellites need to be replaced, after excluding satellites that have already been assigned and selected from all available imaging windows recorded in the preceding steps, the satellite with the highest imaging quality is selected as the "second-best satellite" and reassigned to the sub-block. After replacement, the overlap rate verification process needs to be re-executed until it is confirmed that adjacent sub-blocks meet the sub-block overlap rules under the new scheme.

[0046] Through the above adjustments, we ensure that the actual overlap rate of all adjacent sub-blocks (including horizontal and vertical directions) is within the range of the sub-block overlap rules, and output the final task planning scheme.

[0047] Optionally, the target region is divided to obtain at least one target region sub-block, including: obtaining the imaging swath parameter of each candidate satellite and determining the maximum imaging range of each candidate satellite; determining the effective imaging range of each candidate satellite according to the sub-block overlap rule and the maximum imaging range; performing regularization processing on the original boundary of the target region to obtain a region to be divided with a regular shape; dividing the region to be divided according to the effective imaging range to obtain a preliminary division result; removing the part that does not belong to the target region from the preliminary division result to obtain at least one target region sub-block.

[0048] This embodiment defines the specific method for dividing a large continuous target region into several imaging sub-blocks, laying the foundation for multi-star block imaging.

[0049] Specifically, the imaging swath parameters of the selectable satellites participating in the mission are first obtained to determine the maximum imaging range of the candidate satellites. Assuming the single-satellite imaging swath of the optical satellites within the constellation... If the imaging range is 50km (nadir imaging) and the imaging capability is the same in all directions, then the maximum imaging range (distance) of the candidate satellite in any direction is 50km, and the maximum imaging range (area) is 2500 square kilometers.

[0050] To ensure stitching accuracy, adjacent sub-blocks need to have a certain overlap. In one embodiment, the overlap rate is used as a benchmark. As a sub-block overlap rule, specifically take The effective imaging range (distance) for each candidate satellite is 10%. : That is, in one direction, the effective imaging range (distance) of each sub-block covers a width of 45km, and there is a 5km overlap area between adjacent sub-blocks.

[0051] Next, the irregular polygonal target region is regularized to obtain a region to be divided with a regular shape. The region to be divided needs to include the entire target region; however, to form a regular shape, the region to be divided may also include some additional regions that are not part of the target region. Therefore, the region to be divided should be larger than or equal to the target region.

[0052] Then, the region to be divided is divided according to the effective imaging range to obtain a preliminary division result; in the preliminary division result, the part that does not belong to the target region is removed to obtain at least one target region sub-block.

[0053] In one embodiment, a partitioning strategy using the minimum bounding rectangle plus edge cutting is adopted: In the first stage, the minimum bounding rectangle of the target polygon region is calculated, and this minimum bounding rectangle is used as the initial subdivision region. Rectangular sub-block meshes are generated according to the above-mentioned equal interval division method. In the second stage, an inclusion test is performed on each rectangular sub-block to determine whether its center point is inside the target polygon. Sub-blocks completely outside the polygon are discarded, while those completely inside the polygon and those intersecting the polygon boundary are retained. For edge sub-blocks intersecting the polygon boundary, the intersection area of ​​the sub-block and the polygon boundary is taken as the target region sub-block, and its geographic boundary is the smallest bounding rectangle of the intersection area to ensure the standardized generation of subsequent imaging commands. After the above processing, a set of target region sub-blocks covering the target polygon region is finally obtained.

[0054] In one specific embodiment, such as Figure 2 As shown, taking a 200km×200km area around Hangzhou as an example, the input longitude and latitude range of the target area is: 119.2°~121.8°E, 29.2°~31.2°N, and the total area width is... It is 200km.

[0055] Based on the formula for the effective width of the target region sub-blocks: Calculate the number of sub-blocks required in a single direction based on the total width of the target area: This means the target area needs to be divided into 5 sub-blocks along its width. For a two-dimensional area, this division needs to be done simultaneously along both the longitude and latitude directions. Assuming the target area is square, the total number of sub-blocks is... = 5 × 5 = 25 rectangular target area sub-blocks.

[0056] The geographical boundaries of each sub-block are determined by equally spaced divisions, ensuring a 5km overlap between adjacent sub-blocks at their boundaries. The sub-blocks are numbered as follows: Where i is the row number (1~5) and j is the column number (1~5), each target area sub-block can record the latitude and longitude coordinates of its upper left and lower right corners to represent its position.

[0057] Optionally, for each target region sub-block, the available imaging window of the candidate satellite relative to the target region sub-block is obtained, including: for each candidate satellite, determining the ground visibility band range during the mission period based on the candidate satellite's orbital data and theoretical imaging parameters; for each candidate satellite, statistically analyzing the target region sub-blocks that meet the imaging conditions and fall completely within the candidate satellite's ground visibility band range and the corresponding mission period, and obtaining the available imaging window.

[0058] This embodiment limits the method for obtaining the available imaging window.

[0059] Specifically, based on satellite orbit prediction data and theoretical imaging parameters, the ground area that the satellite can observe during the mission planning period is calculated as the ground visibility band. Because the satellite can actively adjust its side-swing angle to expand its imaging range, this embodiment uses theoretical imaging parameters to determine the ground visibility band, unlike the actual imaging parameters in the previous embodiments.

[0060] Theoretical imaging parameters are theoretical parameters related to the candidate satellite and the imaging equipment on the candidate satellite, such as the maximum side sway angle of the satellite and the maximum rotation angle of the imaging equipment.

[0061] The visible range on the ground is usually a band-shaped area distributed along the trajectory of the satellite's nadir point, and is affected by factors such as the satellite's orbital altitude, the payload's field of view, and the maximum side sway angle.

[0062] Subsequently, for each target area sub-block, it is determined whether it meets the imaging conditions and whether it falls completely within the ground visibility range of a certain satellite, and the corresponding observable time period is recorded. This time period is the available imaging window of the satellite for that sub-block.

[0063] In one embodiment, the imaging condition is considered met when the following imaging conditions are satisfied: (1) Geometric visibility: The observation angle of the center point of the target area sub-block relative to the satellite (i.e., the angle between the satellite-target line and the direction of the satellite's nadir point) does not exceed the maximum side yaw angle of the satellite. ; (2) Solar elevation angle constraint: The solar elevation angle E at the target area sub-block shall not be lower than the preset minimum imaging requirement. To ensure sufficient illumination for optical imaging; (3) Cloud cover constraint: Let C be the specific value of the cloud cover percentage predicted for the target sub-block area. Then C shall not exceed the preset cloud cover percentage threshold. This is to ensure image quality. C represents the specific value for the cloud cover percentage ("%), for example, 20 means the predicted cloud cover percentage is 20%. (4) Time constraint: start and end time of the imaging window Falling within the time frame required by the imaging task Inside.

[0064] Optionally, each target region sub-block is assigned a corresponding candidate satellite and an available imaging window to obtain a preliminary mission planning scheme, including: obtaining reference conditions and actual imaging conditions within each available imaging window; the reference conditions include a reference solar elevation angle and a reference resolution; the actual imaging conditions include the actual solar elevation angle, the actual resolution determined based on the imaging side-swing angle of the candidate satellite, and cloud conditions; based on preset weights, the ratio of the actual solar elevation angle to the reference solar elevation angle, the ratio of the actual resolution to the reference resolution, and the cloud conditions are weighted to obtain an evaluation of the available imaging window; and the highest-rated available imaging window and corresponding candidate satellite are selected for each target region sub-block to obtain a preliminary mission planning scheme.

[0065] This embodiment specifies the method for allocating the satellite with the best imaging conditions to each target region sub-block in order to ensure that the image quality of each target region sub-block is optimal.

[0066] Specifically, based on orbital extrapolation data of the satellite constellation, the orbital positions of all satellites within the constellation are predicted for a future period of time (usually the observation period required by the mission).

[0067] For each target region sub-block Search all available imaging windows for the center point or coverage area of ​​the sub-block from all satellites, and record the following actual imaging conditions for each available imaging window: imaging side sway angle. Actual resolution R (related to side swing angle); actual solar altitude angle E at the time of imaging; cloud cover percentage C, which can be obtained from cloud cover forecast data.

[0068] In one embodiment, cloud conditions are the proportion of cloudless areas, determined by... express.

[0069] To conduct an evaluation, reference conditions are also needed; specifically, the reference solar altitude angle needs to be obtained. and reference resolution .

[0070] In one embodiment, for each available imaging window, the imaging start time also needs to be recorded. and the end of imaging .

[0071] Based on preset weights, the ratio of the actual solar altitude angle to the reference solar altitude angle, the ratio of the actual resolution to the reference resolution, and cloud conditions are weighted to obtain the evaluation Q of the available imaging window: in, , , This is a weighting coefficient, set according to actual needs. In one embodiment, it is taken as... =0.4、 =0.3、 =0.3.

[0072] Since lower resolution values ​​result in clearer images (a 2m object can be recognized at 2m resolution, but not at 2.5m resolution), to ensure a positive correlation between the score and clarity, a [resolution method] is adopted. —When the actual resolution R is better than the reference resolution R_ref (e.g., R=1m, R_ref=2m), the ratio is 2, and the score is higher.

[0073] E_ref is the reference solar altitude angle, representing the solar altitude angle under relatively ideal imaging conditions, such as 45°, while E is the actual solar altitude angle. A higher ratio indicates better lighting conditions.

[0074] For each target region sub-block, select the satellite with the highest imaging quality score Q and its corresponding window from all available imaging windows, and record the allocation results for each sub-block: Sub-block By satellite From time Imaging begins, side tilt angle is The expected imaging quality score is Q, forming a preliminary satellite-sub-block allocation scheme.

[0075] Optionally, the method further includes: within a preset constraint range, optimizing the mission planning scheme based on a preset multi-objective optimization algorithm with the total stitching time, satellite resource utilization rate, and stitching accuracy as optimization objectives. The total stitching time is the time from the start of imaging of the first target region sub-block to the end of imaging of the last target region sub-block. The satellite resource utilization rate is obtained based on the actual used and available resources of the candidate satellites. The stitching accuracy is obtained based on the actual overlap of adjacent target region sub-blocks and the imaging quality of the candidate satellites.

[0076] This embodiment proposes to optimize the mission planning scheme from a global perspective, seeking the optimal balance among the three objectives of total stitching time, satellite resource utilization, and stitching accuracy while satisfying various constraints.

[0077] The method described in this embodiment can be used to optimize a preliminary task planning scheme or a final task planning scheme.

[0078] The preset constraint can be one of the following: Each sub-block must be allocated one satellite and can only be allocated one; Each satellite can only perform one imaging task at a time; The imaging time must be within the available window of the satellite; The total energy consumption of each satellite must not exceed its remaining energy; The total amount of data from each satellite must not exceed its storage capacity.

[0079] The multi-objective optimization model is constructed as follows: Decision variables: each sub-block allocated satellites and the corresponding imaging window W.

[0080] objective function Total splicing time Total splicing time Defined as the time span from the start of imaging the first sub-block to the end of imaging the last sub-block.

[0081] The optimization objective is to minimize the total splicing time. The goal is to minimize the completion time of the entire stitching task and improve the timeliness of observation.

[0082] objective function Satellite resource utilization rate Resource utilization rate U is defined as the balance between the actual resources used and the available resources of each satellite.

[0083] Use load balancing metrics: in _E represents the standard deviation of energy consumption for each satellite, and μ_E represents the average energy consumption; _D represents the standard deviation of data volume for each satellite, and μ_D represents the average data volume. The goal is to minimize the variance of resource usage and achieve load balancing.

[0084] Specifically, the energy consumption E_task for a single imaging mission is calculated as follows: E_task = E_maneuver + E_imaging + E_data, where E_maneuver is the energy consumption for attitude maneuvering (proportional to the change in side angle Δθ, E_maneuver = k_m × |Δθ|, k_m is the energy consumption coefficient for satellite platform maneuvering, unit: J / °), E_imaging is the energy consumption for payload imaging (proportional to the imaging duration t_img, E_imaging = k_i × t_img, k_i is the power consumption of the payload per unit time, unit: J / s), and E_data is the energy consumption for data processing and storage (proportional to the amount of imaging data, E_data = k_d × D_img, k_d is the energy consumption coefficient per unit of data processing, unit: J / MB).

[0085] Calculate the energy consumption of each imaging mission for each satellite and obtain the standard deviation of energy consumption for each satellite. _E and average energy consumption μ_E.

[0086] The formula for calculating the data volume D_img of a single imaging mission is: D_img = (W_sub / GSD) × (L_sub / GSD) × (b / 8), where W_sub is the sub-block width (unit: m), L_sub is the length of the sub-block along the flight direction (unit: m), GSD is the ground sampling distance, i.e., the resolution (unit: m / pixel), b is the number of quantization bits per pixel (e.g., 8 bits for panchromatic and 16 bits for multispectral), and (b / 8) converts the bits into bytes. For example, for a sub-block with a width of 45km, a length of 50km, a resolution of GSD=2m, and 8-bit quantization, the number of pixels is (45000 / 2) × (50000 / 2) = 22500 × 25000 = 562,500,000 pixels, and the data size D_img = 562,500,000 × (8 / 8) = 562,500,000 Byte ≈ 562.5MB.

[0087] Calculate the data volume for each imaging mission and calculate the standard deviation of the data volume from each satellite. And the average data volume μ_D.

[0088] objective function Maximize splicing accuracy The stitching accuracy P is determined by both the overlap ratio deviation and the consistency of image quality. in _target is the target overlap rate (e.g., 12.5%). _Q represents the standard deviation of the imaging quality score for each sub-block, and μ_Q represents the average score. The goal is to minimize overlap deviation and imaging quality differences to ensure optimal stitching results.

[0089] The preset multi-objective optimization algorithm is invoked to solve the problem. The preset multi-objective optimization algorithm can be a specified genetic algorithm or reinforcement learning algorithm.

[0090] In one embodiment, the NSGA-II multi-objective evolutionary algorithm from the genetic algorithm is used, and the specific process is as follows: 1. Encoding: Integer encoding is used, the chromosome length is the total number of sub-blocks, and the value of each gene locus is the satellite number.

[0091] 2. Initialization: Based on the adjusted scheme output in step S3, an initial population of 200 is generated through random mutation.

[0092] 3. Fitness evaluation: Calculate the three objective function values ​​for each individual.

[0093] 4. Non-dominated ranking: Individuals are stratified according to Pareto dominance.

[0094] 5. Crowding Calculation: Calculate the crowding distance between individuals within the same frontal zone.

[0095] 6. Selection, Crossover, and Mutation: Tournament selection, two-point crossover, and random mutation are employed.

[0096] 7. Elite Preservation: Preserves non-dominated solutions from the parent generation to the next generation.

[0097] 8. Iteration: Repeat steps 3-7, with a maximum of 500 iterations.

[0098] 9. Output: Obtain the Pareto optimal solution set for decision-makers to choose from.

[0099] Choose the final solution based on task preferences: If timeliness is a priority, choose... The minimum solution; if the focus is on resource balance, choose The largest solution; if the focus is on the quality of the splicing, choose The largest solution.

[0100] This embodiment selects the comprehensive optimal solution and outputs the optimized task planning scheme.

[0101] Optionally, the method further includes: generating imaging instructions for candidate satellites participating in the mission according to the final mission planning scheme, and coordinating their distribution to the candidate satellites participating in the mission through an inter-satellite communication network; acquiring imaging data and imaging data evaluation results transmitted back by the candidate satellites participating in the mission, wherein the imaging data evaluation results include at least one of the following: imaging matching degree evaluation of adjacent target region sub-blocks, imaging quality evaluation of target region sub-blocks, imaging problems, and stitching processing suggestions.

[0102] This embodiment proposes that after obtaining the final mission planning scheme, imaging instructions can be generated and distributed to the candidate satellites participating in the mission, and imaging data and imaging data evaluation results transmitted back by the satellites can be obtained.

[0103] In one embodiment, imaging instructions are generated for candidate satellites participating in the mission. The instructions include: satellite identifier; latitude and longitude coordinates of the imaging sub-block boundary (coordinates of the upper left and lower right corners); imaging start time (accurate to milliseconds); imaging end time or imaging duration; side sway angle setting (which can be set to a fixed value or a function that varies with time); payload parameter configuration (imaging mode, gain, integration time, etc.); data recording method (recorded in solid-state memory); and data return flag (whether to download in real time).

[0104] In one embodiment, the command formats of different satellite platforms may differ, and can be automatically converted into command formats that can be received by each satellite based on the built-in command template library.

[0105] After the command is generated, it is distributed collaboratively via the inter-satellite communication network. For constellations with inter-satellite links, the following distribution strategy can be adopted: The command packet is sent from the main satellite to the constellation's main satellite via inter-satellite links. Direct command delivery per satellite: Utilizing the telemetry and control arc of each satellite, commands are transmitted separately; Hybrid mode: Prioritize the use of inter-satellite links, with the remaining satellites supplemented by telemetry and control arcs.

[0106] After the command is distributed, the imaging data transmitted back by the satellite is acquired, including: command reception confirmation; attitude maneuver completion status; imaging start / end timestamps; telemetry data during the imaging process (attitude, temperature, voltage, etc.); data recording status, etc.

[0107] In one embodiment, if an abnormality is detected in the execution of a satellite command, an emergency handling mechanism is immediately triggered to reallocate the task of that sub-block.

[0108] The imaging data evaluation results are generated by the stitching pre-verification, which is based on the imaging data initially processed on the satellite to pre-verify the image stitching quality.

[0109] In one embodiment, after the satellite completes imaging, the onboard processing unit performs rapid preprocessing on each sub-block image. After rapid preprocessing, the views and metadata (such as resolution, geometric distortion parameters, grayscale statistical features, etc.) of each target region sub-block image are collected and processed onboard to conduct quality assessment and consistency analysis, and to obtain imaging matching degree evaluation, target region sub-block imaging quality evaluation, imaging problems and / or stitching processing suggestions.

[0110] In one embodiment, rapid preprocessing of each sub-block image includes: Geometric correction (based on satellite attitude and orbit data); Radiation correction (based on on-orbit calibration parameters of the payload); Quick view generation (generates a reduced-resolution preview image for rapid analysis).

[0111] Optionally, the method for obtaining an imaging data evaluation result includes: for an overlapping area of adjacent target area sub-blocks, performing feature point extraction based on a preset feature extraction algorithm to obtain at least one overlapping area feature point set; performing nearest neighbor matching on the regional feature point sets to obtain matched feature point pairs; calculating, for any one of the regional feature point sets, a ratio of the number of matched feature points to the total number of feature points in the set, performing random sample consensus robust estimation on effective matched point pairs, and obtaining an imaging matching degree evaluation; analyzing resolution differences and gray level histograms between images of each target area sub-block, detecting cloud coverage areas and shadow areas in the images of each target area sub-block, and obtaining an imaging quality evaluation of the target area sub-block, an imaging problem and a stitching processing suggestion.

[0112] This embodiment defines the obtaining method for the imaging matching degree evaluation, the target area sub-block imaging quality evaluation, the imaging problem and the stitching processing suggestion in the imaging data evaluation result.

[0113] Specifically, in terms of geometric consistency check, the following steps are used to calculate the imaging matching degree evaluation of adjacent sub-blocks: (1) Feature point extraction: feature point extraction is performed on the quick-view images of two adjacent sub-blocks in the overlapping area respectively, and a preset feature extraction algorithm, such as SIFT (Scale-Invariant Feature Transform) or ORB algorithm, is used to detect key points and their descriptors. Let N_A feature points extracted from sub-block A in the overlapping area form the overlapping area feature point set of A, and N_B feature points extracted from sub-block B form the overlapping area feature point set of B.

[0114] (2) Feature point matching: nearest neighbor matching is performed on the feature point descriptors of sub-block A and sub-block B, and a ratio test is used to filter matched point pairs, that is, only matched point pairs of which the ratio of the best matching distance to the second best matching distance is less than a threshold r (e.g., r=0.7) are retained. Let N_match pairs of effective matching points be obtained after filtering.

[0115] (3) Matching degree calculation: a feature point matching degree M is defined as a ratio of the number of effective matching points to the size of the smaller feature point set, that is, M= N_match / min(N_A, N_B). When M ≥ M_threshold (e.g., M_threshold = 0.6), it is determined that the geometric alignment accuracy meets the requirement; when M<M_threshold, the overlapping area is marked as having a geometric alignment risk.

[0116] (4) Geometric bias estimation: Perform random sampling consensus (RANSAC) robust estimation on valid matching point pairs, calculate the homography matrix H, and count the number of inliers N_inlier and the reprojection error RMSE. When RMSE > RMSE_threshold (e.g., RMSE_threshold = 2 pixels), the marker indicates the presence of a geometrically distorted region.

[0117] In one specific embodiment, taking the overlapping area of ​​the Qiantang River between sub-block B_33 and its adjacent sub-block B_34 in the main urban area of ​​Hangzhou as an example, sub-block B_33 extracted 120 feature points in the overlapping area, while sub-block B_34 extracted 115 feature points. After ratio testing and screening, 82 pairs of valid matching points were obtained. The matching degree M = 82 / min(120, 115) = 82 / 115 ≈ 0.713 > 0.6, indicating that the geometric alignment accuracy meets the requirements. RANSAC estimation yielded a reprojection error RMSE of 1.3 pixels < 2 pixels, further confirming the good geometric alignment quality and forming an imaging matching degree evaluation.

[0118] Analyze the resolution differences between each sub-block image. When the difference exceeds a set threshold (e.g., 5%), mark the possible geometric distortion areas and detect the stitching seams at the sub-block boundaries to assess whether there is obvious geometric misalignment.

[0119] For radiometric consistency verification, the grayscale histogram features of each sub-block image are statistically analyzed, and statistical measures such as mean and variance are calculated. The grayscale differences between adjacent sub-blocks in overlapping areas are also analyzed. When the grayscale difference exceeds a preset threshold, potential radiometric inconsistencies are marked. At the same time, cloud cover areas and shadow areas that may exist in the image are detected, and their impact on stitching quality is evaluated.

[0120] Based on the above analysis results, imaging data evaluation results are generated, mainly including: Target region sub-block imaging quality evaluation Target region sub-block imaging quality evaluation Imaging issues (such as the location of seams, areas of geometric deviation, and areas of radiation difference). Suggestions for splicing processing (such as color matching or geometric correction). The evaluation results of imaging data can serve as an important basis for subsequent image stitching processing, guiding the selection of stitching parameters and optimization of processing strategies, thereby improving the efficiency and quality of image stitching and realizing autonomous processing and quality control of image data.

[0121] In one specific embodiment, the multi-satellite region stitching imaging task planning and imaging implementation are based on the methods of all the foregoing embodiments, such as... Figure 3 As shown, the specific steps are as follows: Step S1: Target area meshing step Step S2, Optimal Satellite Allocation Step S3: Verification and Adjustment of Splicing Overlap Rate Step S4: Intelligent Optimization Engine Scheduling Step S5: Instruction Distribution and Cooperative Control Step S6: Pre-verification of splicing.

[0122] The third embodiment of this application also proposes a multi-satellite region stitching imaging task planning device, such as... Figure 4 As shown, the device includes: The segmentation unit 410 is used to segment the target region to obtain at least one target region sub-block, wherein the segmentation is such that the size of each target region sub-block matches the imaging swath width of the candidate satellite, and the overlapping area between adjacent target region sub-blocks satisfies a preset sub-block overlap rule. The window acquisition unit 420 is used to acquire the available imaging window of the candidate satellite relative to the target area sub-block for each target area sub-block. The available imaging window is the time interval during which the payload field of view can cover the target area sub-block during the transit of the candidate satellite. Planning unit 430 is used to allocate corresponding candidate satellites and available imaging windows to each target area sub-block to obtain a preliminary mission planning scheme, wherein the allocation is based on the imaging quality of the available imaging windows; The verification unit 440 is used to determine the actual overlap for each group of adjacent target area sub-blocks based on the preliminary task planning scheme, and to verify the actual overlap based on the sub-block overlap rules, thereby generating the final task planning scheme.

[0123] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0124] In this embodiment, the multi-satellite region stitching imaging task planning device is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0125] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 5As shown, the computer device includes one or more processors 510, memory 520, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take the 510 processor as an example.

[0126] Processor 510 may be a central processing unit, a network processor, or a combination thereof. Processor 510 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0127] The memory 520 stores instructions executable by at least one processor 510 to cause the at least one processor 510 to perform the method shown in the above embodiments.

[0128] The memory 520 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 520 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 520 may optionally include memory remotely located relative to the processor 510, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] The memory 520 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 520 may also include a combination of the above types of memory.

[0130] The computer device also includes a communication interface 530 for communicating with other devices or communication networks.

[0131] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0132] This application also provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.

[0133] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

[0134] The methods, apparatus, computer devices, computer-readable storage media, or computer program products described in the above embodiments can be implemented by a computer chip or entity, or by a product having a certain function. A typical implementing device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0135] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0136] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, computer devices, computer-readable storage media, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0137] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, computer devices, computer-readable storage media, or computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0138] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0139] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0140] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0141] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, computer equipment, computer-readable storage media, or computer program products are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0142] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0143] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for planning multi-star region stitching imaging tasks, characterized in that, The method includes: The target region is divided to obtain at least one target region sub-block, wherein the division is such that the size of each target region sub-block matches the imaging swath width of the candidate satellite, and the overlapping area between adjacent target region sub-blocks satisfies a preset sub-block overlap rule, wherein the sub-block overlap rule is a preset reasonable range for the area of ​​the overlapping area. For each target region sub-block, the available imaging window of the candidate satellite relative to the target region sub-block is obtained. The available imaging window is the time interval during which the payload's field of view can cover the target region sub-block during the candidate satellite's transit. Each target region sub-block is assigned a corresponding candidate satellite and an available imaging window to obtain a preliminary mission planning scheme, wherein the assignment is based on the imaging quality of the available imaging window; For each group of adjacent target area sub-blocks, the actual overlap is determined according to the preliminary task planning scheme, and the actual overlap is verified based on the sub-block overlap rules to generate the final task planning scheme. The step of verifying the actual overlap based on the sub-block overlap rules and generating the final task planning scheme includes: For each group of adjacent target region sub-blocks whose actual overlap rate does not meet the sub-block overlap rule, a preset adjustment operation is performed and the actual overlap rate is recalculated until the recalculated actual overlap rate meets the sub-block overlap rule. The preset adjustment operation includes at least one of the following: adjusting the actual imaging parameters of the candidate satellite, adjusting the boundary of the target region sub-block, and replacing the candidate satellite. The task planning scheme in which the actual overlap rate of adjacent target area sub-blocks all meets the sub-block overlap rule is taken as the final task planning scheme. The process of assigning corresponding candidate satellites and available imaging windows to each target region sub-block to obtain a preliminary mission planning scheme includes: The reference conditions and actual imaging conditions within each available imaging window are obtained. The reference conditions include a reference solar elevation angle and a reference resolution. The actual imaging conditions include the actual solar elevation angle, the actual resolution determined based on the imaging side-swing angle of the candidate satellite, and cloud conditions. Based on preset weights, the ratio of the actual solar altitude angle to the reference solar altitude angle, the ratio of the actual resolution to the reference resolution, and cloud conditions are weighted to obtain an evaluation of the available imaging window. For each target region sub-block, select the highest-rated available imaging window and the corresponding candidate satellite to obtain the preliminary mission planning scheme.

2. The method according to claim 1, characterized in that, For each group of adjacent target region sub-blocks, determining the actual overlap based on the preliminary task planning scheme includes: For each group of adjacent target area sub-blocks, the actual imaging range of the candidate satellite on the adjacent target area sub-blocks is determined based on the actual imaging parameters of at least one candidate satellite allocated to the target area sub-block in the preliminary mission planning scheme. Based on the actual imaging range, the actual overlap rate of the adjacent target region sub-blocks is determined.

3. The method according to claim 1, characterized in that, The step of dividing the target region to obtain at least one target region sub-block includes: Obtain the imaging swath width parameter for each candidate satellite and determine the maximum imaging range for each candidate satellite; The effective imaging range of each candidate satellite is determined based on the sub-block overlap rule and the maximum imaging range; The original boundary of the target region is regularized to obtain a region to be divided with a regular shape; The region to be divided is divided according to the effective imaging range to obtain preliminary division results; Remove portions that do not belong to the target region from the preliminary division results to obtain at least one target region sub-block.

4. The method according to claim 1, characterized in that, The step of obtaining the available imaging window of the candidate satellite relative to the target region sub-block for each target region sub-block includes: For each candidate satellite, the ground visibility band during the mission period is determined based on the candidate satellite's orbital data and theoretical imaging parameters. For each candidate satellite, the target area sub-blocks that meet the imaging conditions and fall completely within the ground visibility range of the candidate satellite, along with the corresponding mission time period, are counted to obtain the available imaging window.

5. The method according to claim 1, characterized in that, The method further includes: Within the preset constraints, the mission planning scheme is optimized based on a preset multi-objective optimization algorithm, with the total stitching time, satellite resource utilization rate, and stitching accuracy as optimization objectives. The total stitching time is the time from the start of imaging of the first target region sub-block to the end of imaging of the last target region sub-block. The satellite resource utilization rate is obtained based on the actual used and available resources of the candidate satellites. The stitching accuracy is obtained based on the actual overlap of adjacent target region sub-blocks and the imaging quality of the candidate satellites.

6. The method according to claim 1, characterized in that, The method further includes: Based on the final mission planning scheme, imaging commands are generated for the candidate satellites participating in the mission and distributed to the candidate satellites participating in the mission in a coordinated manner through the inter-satellite communication network; The imaging data and imaging data evaluation results returned by the candidate satellites participating in the mission are obtained. The imaging data evaluation results include at least one of the following: imaging matching degree evaluation of adjacent target area sub-blocks, imaging quality evaluation of target area sub-blocks, imaging problems, and stitching processing suggestions.

7. The method according to claim 6, characterized in that, The methods for obtaining the imaging data evaluation results include: For overlapping areas of adjacent target sub-blocks, feature points are extracted based on a preset feature extraction algorithm to obtain at least one set of feature points for the overlapping area. Nearest neighbor matching is performed on the set of feature points in the region to obtain matching feature point pairs; For any set of feature points in a region, calculate the ratio of the number of matching feature points to the total number of feature points in the set, perform random sampling consistent robust estimation on the effective matching point pairs, and obtain the imaging matching degree evaluation. The resolution differences and grayscale histograms between the images of each target region sub-block are analyzed to detect cloud-covered and shadowed areas in the images of each target region sub-block, and to obtain the imaging quality evaluation of the target region sub-block, the imaging problems, and the stitching processing suggestions.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the multi-satellite region stitching imaging task planning method according to any one of claims 1 to 7.

Images