A remote sensing data coverage screening method based on benefit value

Abstract

The present invention relates to a remote sensing data coverage screening method based on benefit value, and relates to the field of remote sensing image data processing technology, and solves technical problems existing in the prior art, such as insufficient regional coverage optimization, serious waste of computing resources, and lack of flexible screening strategies. The method comprises the following steps: Step 1: preparing a data set; Step 2: checking the attributes of the data set; Step 3: regional coverage algorithm. The remote sensing data coverage screening method based on benefit value of the present invention significantly improves the efficiency and accuracy of remote sensing image data selection by designing a coverage algorithm and introducing image quality control and consistency assurance mechanisms. The method of the present invention can reduce the storage and computing burden of redundant images, maximize the efficiency of image coverage, and ensure the accuracy and consistency of global surface coverage.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image data processing, and in particular to a remote sensing data coverage screening method based on revenue value. Background Art

[0002] In terms of remote sensing image data selection and coverage optimization, there are certain research and methods in the existing technology, but there are still some deficiencies in the following aspects:

[0003] 1. Inconsistent time phase and inefficient image selection.

[0004] Current image overlay methods often employ simple overlay models. First, they fail to consider the principle of co-orbit, leading the algorithm to select a large number of images from different satellites captured at different times within a specific area. Due to differences in satellite capture times, changes in terrain and topography, shooting angles, solar altitudes, and other operating conditions, the color and content of objects in the images of that area vary, affecting the image quality and accuracy of the final product. Furthermore, simple overlay models fail to consider minimizing overlay redundancy, resulting in high consumption of computing resources and storage space when faced with large datasets. This ultimately results in the stacking of a large number of low-utilization images within a small area, resulting in a significant amount of duplicate computation and unnecessary data storage.

[0005] 2. Lack of flexible image selection strategy.

[0006] Most existing image selection strategies are static and cannot be dynamically adjusted according to specific circumstances. For example, when selecting images, they fail to flexibly select appropriate images based on the special requirements of the region (such as latitude, seasonal differences, etc.).

[0007] In summary:

[0008] The shortcomings of existing technologies are mainly reflected in insufficient area coverage optimization, serious waste of computing resources and lack of flexible screening strategies. Summary of the Invention

[0009] The present invention aims to solve the technical problems existing in the prior art, such as insufficient optimization of regional coverage, serious waste of computing resources and lack of flexible screening strategies, and provides a remote sensing data coverage screening method based on benefit value.

[0010] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0011] A remote sensing data coverage screening method based on benefit value comprises the following steps:

[0012] Step 1: Prepare the dataset;

[0013] Prepare a remote sensing data set within a specified time interval; the target area is , the entire dataset is the dataset , filter out the ones that can cover the target area The optimized sub-dataset ;

[0014] Step 2: Check the data set attributes;

[0015] Ensure the dataset Each image in has the following attributes: image coverage area vector, image quality, image cloud cover, shooting time and satellite model;

[0016] Step 3: Area coverage algorithm;

[0017] Based on the greedy algorithm, from the data set Filter the data until the optimal sub-dataset is selected , able to fully cover the target area , or dataset Until there is no other suitable data.

[0018] In the above technical solution, the optimized sub-dataset in step 1 satisfy:

[0019] Full coverage target ;

[0020] Optimizing subdatasets Remote sensing images The repetition rate between each other is low. ;

[0021] There is consistency between adjacent data images;

[0022] Use high-quality data with low cloud cover.

[0023] In the above technical solution, the image quality in step 2 is divided into four levels: A, B, C, and D; the image cloud cover is expressed as a value in the range of 0-100%.

[0024] In the above technical solution, the specific steps of step 3 are:

[0025] Step 3.1: Create an image collection;

[0026] Traverse the image database and select the target area The set of all images that intersect is the dataset ;

[0027] Step 3.2: Group by shooting task;

[0028] The filtered images are grouped by satellite model and shooting time, and each group is represented as ;

[0029] Step 3.3: Calculate the single-track image coverage gain;

[0030] For each group Each remote sensing image in , according to its quality grade factor , cloud cover , shooting time and the overlapping area with the target area, calculate the remote sensing image Effective coverage income , the calculation formula is as follows:

[0031] ;

[0032] in, For the optimal shooting time, For remote sensing images The quality grade factor, For images Repeat coverage loss;

[0033] Calculate the effective coverage gain of the same-track image The formula is as follows:

[0034] ;

[0035] in, is the total number of images in a single track, is the shooting time of a single track image, Repeat coverage loss for the entire track;

[0036] Step 3.4: Update the target area;

[0037] After selecting the single track with the maximum effective coverage benefit, update the uncovered area , remove the covered part of the selected image;

[0038] Step 3.5: Circular calculation;

[0039] In the initial target area The effective coverage benefit of the optimal single-track image is determined in Then, the root mean square error is used to calculate the phase difference. The calculation formula is as follows:

[0040] ;

[0041] Will Normalize and substitute into the above formula to get:

[0042] ;

[0043] in, For subsequent selection of imaging time for single-track images, is the shooting time of the single track image of the initial optimal single track, The total number of selected tracks.

[0044] The present invention has the following beneficial effects:

[0045] The present invention's benefit-value-based remote sensing data coverage screening method significantly improves the efficiency and accuracy of remote sensing image data selection by designing a coverage algorithm and introducing image quality control and consistency assurance mechanisms. Compared to existing technologies, the present invention's method can reduce the storage and computational burden of redundant images, maximize image coverage efficiency, and ensure the accuracy and consistency of global surface coverage. More specifically, the beneficial effects include:

[0046] 1. Image quality and cloud screening accuracy.

[0047] The yield-based remote sensing data coverage screening method of this invention assumes that images have already undergone precise quality inspection and are accurately labeled with quality grades (A, B, C, D) and cloud cover percentages. This pre-calibration approach eliminates the need for additional quality screening during image selection, thus avoiding repeated image quality checks and significantly improving data processing efficiency. Furthermore, accurate cloud cover detection allows for more precise exclusion of images with heavy cloud cover during image selection, ensuring clarity in the coverage area.

[0048] 2. Efficiency of area coverage and image selection.

[0049] The present invention's benefit-based remote sensing data coverage screening method optimizes image selection through a greedy algorithm, ensuring that all target areas are covered by the most appropriate set of images while minimizing image overlap. Compared to the simple coverage model of existing technologies, this method reduces the use of redundant images, maximizes coverage efficiency for each area, reduces the waste of computing resources, and improves data efficiency.

[0050] 3. Ensure image consistency.

[0051] The present invention's yield-based remote sensing data coverage screening method ensures temporal image consistency and ground color consistency by prioritizing images from the same orbital mission. Unlike existing techniques that often suffer from inter-image lighting differences and color inconsistencies, the present method ensures high consistency in hue and lighting across selected images, providing a more accurate and coherent image of ground cover, ensuring consistent image quality and visual quality.

[0052] 4. Optimization of computing resources and storage efficiency.

[0053] The present invention's benefit-based remote sensing data coverage screening method employs a greedy algorithm and image selection strategy to ensure that only the optimal set of images is selected for each region, avoiding the storage and computation of redundant images. Compared to the potential duplicate computation and redundant storage issues encountered in existing technologies, the present method significantly reduces computational and storage burdens, improving data processing efficiency and storage utilization.

[0054] 5. Flexible image selection strategy.

[0055] The present invention's revenue-based remote sensing data coverage screening method provides a dynamic image selection framework, flexibly adjusting image selection strategies based on regional characteristics (such as latitude, seasonality, and satellite orbits) to ensure optimal coverage. This flexibility enables the present method to adapt to the specific needs of different geographic regions, providing a more accurate and efficient image selection solution and overcoming the limitations of existing static selection methods. DETAILED DESCRIPTION

[0056] The inventive concept of the present invention is:

[0057] The benefit-value-based remote sensing data coverage screening method of the present invention first subjects images to precise quality inspection and labels them with quality grades (A, B, C, D) and cloud cover percentages. Secondly, based on a greedy algorithm, a coverage strategy is determined that prioritizes same-track coverage and minimizes overlap, thereby maximizing temporal consistency and reducing image overlap. Finally, the present invention provides a dynamic selection framework that can flexibly adjust image selection strategies based on specific regional characteristics (such as latitude, season, and time requirements) to ensure optimal coverage.

[0058] The present invention is described in detail below.

[0059] The remote sensing data coverage screening method based on revenue value of the present invention comprises the following steps:

[0060] Step 1: Prepare the dataset;

[0061] Prepare a remote sensing data set within a specified time interval, such as the 2024 global remote sensing data set and the 2023 China remote sensing data set; theoretically ensure that all data in the data set can cover the target area, and set the target area as , the full dataset is , filter out the ones that can cover the target area The optimized sub-dataset .

[0062] The goal of the remote sensing data coverage screening method based on revenue value of the present invention is to optimize the sub-dataset , the optimized sub-dataset The characteristics are as follows:

[0063] Cover as many targets as possible ;

[0064] Optimizing subdatasets Remote sensing images The duplication rate between each other is as low as possible;

[0065] The consistency (time) between adjacent data images is as close as possible;

[0066] Try to use data with high quality and low cloud cover;

[0067] Step 2: Check the data set attributes;

[0068] Ensure the dataset Each image has the following attributes: image coverage area vector, image quality (A, B, C or D), image cloud cover (0-100%), shooting time and satellite model;

[0069] Step 3: Area coverage algorithm;

[0070] Based on the greedy algorithm, from the data set Filter the data until the optimal sub-dataset is selected , able to fully cover the target area , or dataset Until there is no other suitable data.

[0071] The relevant concepts are explained as follows:

[0072] Effective coverage income : Used to evaluate the image in the target area The coverage quality is affected by cloud amount, quality and effective coverage area. The larger the benefit value, the greater the image's contribution to coverage.

[0073] Repeat coverage loss : Used to evaluate image validity. It is defined as the coverage area where the image is invalid, that is, the ratio of the overlap between the image and the already covered area to the total image area. The larger this loss, the less valid the image.

[0074] Quality grade factor : Define quality level factors based on the different quality levels of the image. When overwriting, the best choice is images with quality levels A and B, with level C as an alternative and D as much as possible not to be selected. Among them, quality level A: , quality grade is B: , quality level is C: , quality grade is D: .

[0075] Phase difference loss : Used to evaluate the selected dataset The temporal quality of the internal image. When selecting images, for low-latitude areas, you can use data from the whole year. For mid-latitude areas, it is best to use summer data, followed by spring and autumn. For high-latitude areas, try to use only summer data. In addition, the selected dataset should also be considered. The phase difference of the inner image is ensured to be close.

[0076] The algorithm content is as follows:

[0077] enter:

[0078] Target area : target area to be covered;

[0079] Dataset ;

[0080] The image's boundaries (latitude and longitude range);

[0081] Satellite model and time of image capture (used for grouping on the same track);

[0082] The cloudiness of the image (affects the yield value);

[0083] The quality level of the image (A, B, C, D).

[0084] Output:

[0085] Optimizing subdatasets :The selected ones can cover the target area The optimal image set.

[0086] The algorithm steps are as follows:

[0087] Step 3.1: Create an image collection;

[0088] Traverse the image database and select the target area The set of all images that intersect is the dataset .

[0089] Step 3.2: Group by shooting task;

[0090] The filtered images are grouped by satellite model and shooting time, and the shooting tasks (i.e., images on the same track) are divided into groups. Each group is represented as The images in each group belong to the same satellite and the same shooting mission, and have the same or similar timestamps.

[0091] Step 3.3: Calculate the single-track image coverage gain;

[0092] To calculate the overall coverage benefit of a certain track image set, it is necessary to first calculate the coverage benefits of all images in the track. Each remote sensing image in , according to its quality grade factor , cloud cover , shooting time and the overlapping area with the target area, calculate the remote sensing image Effective coverage income , the calculation formula is as follows:

[0093] ;

[0094] in, For the optimal shooting time, For remote sensing images The quality grade factor, For images Repeat coverage loss;

[0095] Calculate the effective coverage gain of the same-track image The formula is as follows:

[0096] ;

[0097] in, is the total number of images in a single track, is the shooting time of a single track image, The overlap loss for the entire track is different from the calculation for a single scene. Images within the same track may overlap at their edges. The overlap loss for the entire track is the ratio of the effective coverage area of ​​the entire track to the image area of ​​the entire track.

[0098] Step 3.4: Update the target area;

[0099] After selecting the single track with the maximum effective coverage benefit, it is necessary to update the uncovered area , that is, remove the covering part of the selected image.

[0100] Step 3.5: Circular calculation;

[0101] In the initial target area The effective coverage benefit of the optimal single-track image is determined in Finally, make sure that the other tracks are The phase difference is close, and the method of the present invention uses the root mean square error to calculate the phase difference. The calculation formula is as follows:

[0102] ;

[0103] Will Normalize and substitute into the above formula to get:

[0104] ;

[0105] in, For subsequent selection of imaging time for single-track images, is the shooting time of the single track image of the initial optimal single track, = is the total number of selected tracks. When calculating the effective coverage gain of each newly added single-track image, the imaging time of all selected single tracks needs to be considered.

[0106] In summary, the single track with the highest effective coverage benefit is selected multiple times and the target area is updated multiple times until the uncovered area is or , that is, the entire area is covered or there is no valid image. All used in-track images are merged into the final target optimization sub-dataset .

[0107] The present invention's benefit-based remote sensing data coverage screening method significantly improves the efficiency and accuracy of remote sensing image data selection by designing a coverage algorithm and introducing image quality control and consistency assurance mechanisms. Compared to existing technologies, this method reduces the storage and computational burden of redundant images, maximizes image coverage efficiency, and ensures accurate and consistent global surface coverage.

[0108] The yield-based remote sensing data coverage screening method of this invention assumes that images have already undergone precise quality inspection and are accurately labeled with quality grades (A, B, C, D) and cloud cover percentages. This pre-calibration approach eliminates the need for additional quality screening during image selection, thus avoiding repeated image quality checks and significantly improving data processing efficiency. Furthermore, accurate cloud cover detection allows for more precise exclusion of images with heavy cloud cover during image selection, ensuring clarity in the coverage area.

[0109] The present invention's benefit-based remote sensing data coverage screening method optimizes image selection through a greedy algorithm, ensuring that all target areas are covered by the most appropriate set of images while minimizing image overlap. Compared to the simple coverage model of existing technologies, this method reduces the use of redundant images, maximizes coverage efficiency for each area, reduces the waste of computing resources, and improves data efficiency.

[0110] The present invention's yield-based remote sensing data coverage screening method ensures temporal image consistency and ground color consistency by prioritizing images from the same orbital mission. Unlike existing techniques that often suffer from inter-image lighting differences and color inconsistencies, the present method ensures high consistency in hue and lighting across selected images, providing a more accurate and coherent image of ground cover, ensuring consistent image quality and visual quality.

[0111] The present invention's benefit-based remote sensing data coverage screening method employs a greedy algorithm and image selection strategy to ensure that only the optimal set of images is selected for each region, avoiding the storage and computation of redundant images. Compared to the potential duplicate computation and redundant storage issues encountered in existing technologies, the present method significantly reduces computational and storage burdens, improving data processing efficiency and storage utilization.

[0112] The present invention's revenue-based remote sensing data coverage screening method provides a dynamic image selection framework, flexibly adjusting image selection strategies based on regional characteristics (such as latitude, seasonality, and satellite orbits) to ensure optimal coverage. This flexibility enables the present method to adapt to the specific needs of different geographic regions, providing a more accurate and efficient image selection solution and overcoming the limitations of existing static selection methods.

[0113] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A remote sensing data coverage screening method based on revenue value, characterized in that: The following steps are involved: Step 1: Prepare the dataset; Prepare remote sensing data sets within a specified time interval; The target area is R, the full dataset is dataset S, and the optimal sub-dataset s that can cover the target area R is selected; Step 2: Check the data set attributes; Ensure that each image in the dataset S has the following attributes: image coverage vector, image quality, image cloud cover, shooting time and satellite model; Step 3: Area coverage algorithm; Based on the greedy algorithm, data is filtered from the dataset S until the optimal sub-dataset s is selected, which can completely cover the target area R, or there is no other suitable data in the dataset S; Step 3.1: Create an image collection; Traverse the image database and select all images that intersect with the target area R as the dataset S; Step 3.2: Group by shooting task; The filtered images are grouped into shooting tasks according to satellite model and shooting time, and each group is represented as P; Step 3.3: Calculate the single-track image coverage gain; For each remote sensing image p in each group P i , according to its quality grade factor q i , cloud cover c i , shooting time t i and the overlapping area with the target area, calculate the remote sensing image p i Effective coverage income g i , the calculation formula is as follows: Among them, t' is the optimal shooting time, q i is the remote sensing image p i The quality level factor, L i For image p i Repeat coverage loss; The formula for calculating the effective coverage benefit G of the same-track image is as follows: Where n is the total number of images in a single track, t is the capture time of a single track image, and L is the repeated coverage loss of the entire track; Step 3.4: Update the target area; After selecting the single track with the maximum effective coverage gain, the uncovered area R′ is updated to remove the covered part of the selected image; Step 3.5: Circular calculation; After determining the effective coverage gain G0 of the optimal single-track image in the initial target area R, the root mean square error (RMSE) is used to calculate the temporal difference. The root mean square error (RMSE) calculation formula is as follows: Normalizing the RMSE and substituting it into the above formula yields: Among them, t j is the imaging time of the subsequent selected single-track image, t is the imaging time of the single-track image of the initial optimal single track, and m is the total number of selected tracks.

2. The remote sensing data coverage screening method based on revenue value according to claim 1, characterized in that: The optimized sub-dataset s in step 1 satisfies: Complete coverage of target A; Optimize the remote sensing images p in the sub-dataset s i The repetition rate between each other is low, i = 1, 2, ... n, p i ∈s; There is consistency between adjacent data images; Use high-quality data with low cloud cover.

3. The remote sensing data coverage screening method based on revenue value according to claim 1, characterized in that: The image quality in step 2 is divided into four levels: A, B, C, and D; the cloudiness of the image is expressed as a value in the range of 0-100%.

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