Remote sensing data coverage screening method based on profit value

Abstract

The invention relates to a remote sensing data coverage screening method based on a profit value, relates to the technical field of remote sensing image data processing, and solves the technical problems of insufficient region coverage optimization, serious computing resource waste, lack of flexible screening strategies and the like in the prior art. The method comprises the following steps: step 1, preparing a data set; 2, checking attributes of the data set; and step 3, performing a region coverage algorithm. According to the remote sensing data coverage screening method based on the profit value, by designing a coverage algorithm and introducing an image quality control and consistency guarantee mechanism, the efficiency and accuracy of remote sensing image data selection are remarkably improved. According to the method, the storage and calculation burden of redundant images can be reduced, the efficiency of image coverage is maximized, and the accuracy and consistency of global earth surface coverage are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image data processing, and particularly to a method for screening remote sensing data coverage based on benefit values. Background Art

[0002] In the selection and coverage optimization of remote sensing image data, there have been certain studies and methods in the prior art, but there are still the following deficiencies:

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

[0004] The existing image coverage methods often adopt simple coverage models. First, the principle of the same orbit is not considered, which leads to the algorithm selecting a large number of images from different satellite shooting tasks at different times within a certain area. Due to different working conditions such as the time difference of satellite shooting, changes in ground features and landforms, shooting angles, and solar altitude angles, the ground feature colors and ground feature contents of the images in this area are different, affecting the quality of the final product image and the accuracy of image content. In addition, the simple coverage model does not consider minimizing coverage redundancy. When facing a large data set, the consumption of computing resources and storage space is relatively large. Eventually, a large number of underutilized images are stacked within a small area, using a large amount of repeated calculations and unnecessary data storage.

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

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

[0007] In summary:

[0008] The deficiencies of the prior art are mainly reflected in insufficient regional coverage optimization, serious waste of computing resources, and lack of a flexible screening strategy. Summary of the Invention

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

[0010] To solve the above technical problems, the technical solution of the present invention is specifically as follows:

[0011] A method for screening remote sensing data coverage based on benefit values, comprising the following steps:

[0012] Step 1: Prepare a data set;

[0013] Prepare a remote sensing data set within a specified time interval; the target area is , the complete data set is the data set , and filter out the optimal sub-data set that can cover the target area ; ;

[0014] Step 2: Data set attribute check;

[0015] Ensure that each image in the data set 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, filter data from the data set until the optimal sub-data set is selected, which can completely cover the target area , or until there is no other suitable data in the data set .

[0018] In the above technical solution, the optimal sub-data set in Step 1 satisfies:

[0019] Completely cover the target ;

[0020] The optimal sub-data set has a low repetition rate among the remote sensing images ; ;

[0021] There is consistency between adjacent data images;

[0022] Use data with high quality level and 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 represented as a value within 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 set;

[0026] Traverse the image database and select all image sets that intersect with the target area as the data set ;

[0027] Step 3.2: Group by shooting task;

[0028] Group the selected images according to satellite model and shooting time for shooting tasks, and each group is represented as ;

[0029] Step 3.3: Calculate the effective coverage benefit of single-track images;

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

[0031] ;

[0032] Among them, is the optimal shooting time, is the quality level factor of the remote sensing image , is the repeated coverage loss of the image ;

[0033] Calculate the effective coverage benefit of images on the same track The formula is as follows:

[0034] ;

[0035] Among them, is the total number of images within a single track, is the shooting time of the single-track image, is the repeated coverage loss of 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 , and remove the covered part of the selected images;

[0038] Step 3.5: Loop calculation;

[0039] After determining the effective coverage benefit of the optimal single-track image in the initial target area , use the root mean square error to calculate the temporal difference, and the root mean square error The calculation formula is as follows:

[0040] ;

[0041] After normalizing and substituting it into the above formula, we get:

[0042] ;

[0043] wherein, is the imaging time for subsequent selection of 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.

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

[0045] 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 an image quality control and consistency guarantee mechanism. Compared with the prior art, the method of the present invention can reduce the storage and calculation burden of redundant images, maximize the efficiency of image coverage, and ensure the accuracy and consistency of global land cover. More specifically, the beneficial effects specifically include:

[0046] 1. Precision of image quality and cloud amount screening.

[0047] The remote sensing data coverage screening method based on benefit value of the present invention assumes that the images have passed precise quality inspections and are marked with accurate quality grades (A, B, C, D) and cloud amount percentages. This pre-calibration method ensures that no additional quality screening steps are required during image selection, thus avoiding repeated detection of image quality and significantly improving data processing efficiency. In addition, accurate cloud amount detection enables more precise exclusion of images with large cloud amounts when selecting images, ensuring the clarity of the coverage area.

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

[0049] The remote sensing data coverage screening method based on benefit value of the present invention optimizes image selection through a greedy algorithm to ensure that all target areas are covered by the most suitable image set as much as possible while minimizing image overlap. Compared with the simple coverage model of the prior art, the method of the present invention can reduce the use of redundant images, maximize the coverage efficiency of each area, reduce waste of computing resources, and improve the benefit of data use.

[0050] 3. Guarantee of image consistency.

[0051] The method for screening remote sensing data coverage based on benefit value of the present invention ensures the temporal consistency of images and the consistency of ground colors by preferentially selecting images of the same orbit mission. Different from the problems of illumination differences and color inconsistencies between images existing in the prior art, the method of the present invention can ensure a high degree of consistency in hue and illumination of the selected images, thereby providing a more accurate and coherent surface coverage image and ensuring the consistency of image quality and visual effects.

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

[0053] The method for screening remote sensing data coverage based on benefit value of the present invention adopts a greedy algorithm and an image selection strategy to ensure that only the optimal image set is selected for each area, avoiding the storage and calculation of redundant images. Compared with the problems of repeated calculation and storage redundancy that may occur in the prior art, the method of the present invention significantly reduces the computing and storage burdens and improves the data processing efficiency and storage utilization rate.

[0054] 5. Flexible image selection strategy.

[0055] The method for screening remote sensing data coverage based on benefit value of the present invention provides a dynamic image selection framework, which flexibly adjusts the image selection strategy according to the characteristics of different areas (such as latitude, seasonality, satellite orbit, etc.) to ensure the best coverage effect. This flexibility enables the method of the present invention to adapt to the special requirements of different geographical areas, provides a more accurate and efficient image selection scheme, and breaks through the limitations of the static selection method in the prior art. Detailed implementation mode

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

[0057] For the method for screening remote sensing data coverage based on benefit value of the present invention, firstly, the images are precisely quality inspected, and the quality grades (A, B, C, D) and cloud amount percentages are marked; secondly, based on the greedy algorithm, a coverage strategy of preferentially selecting the same orbit and minimizing overlap is determined to improve the temporal consistency as much as possible and reduce image overlap; finally, the present invention provides a framework for dynamic selection, which can flexibly adjust the image selection strategy according to the specific characteristics of the area (such as latitude, season, time requirements, etc.) to ensure the best coverage effect.

[0058] The present invention will be described in detail below.

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

[0060] Step 1: Prepare the data set;

[0061] Prepare a set of remote sensing data within a specified time interval, such as the global remote sensing data set in 2024 and the Chinese remote sensing data set in 2023; theoretically, ensure that all data in the data set can cover the target area. Let the target area be , and the data set of the whole set is , and filter out the sub-data set that can cover the target area with the best optimization .

[0062] The method for screening remote sensing data coverage based on the revenue value of the present invention aims at the sub-data set with the best optimization , and the characteristics of this sub-data set with the best optimization are as follows:

[0063] Cover the target as completely as possible ;

[0064] In the sub-data set with the best optimization , the repetition rate between each remote sensing image 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 level and low cloud amount;

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

[0068] Ensure that each image in the data set has the following attributes: image coverage area vector, image quality (A, B, C or D), image cloud amount (0-100%), shooting time and satellite model;

[0069] Step 3: Regional coverage algorithm;

[0070] Based on the greedy algorithm, filter data from the data set until the sub-data set with the best optimization that can completely cover the target area is selected, or until there is no other suitable data in the data set .

[0071] The relevant concept explanations are as follows:

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

[0073] Repeated coverage loss : Used to evaluate the validity of an image, defined as the covered area where the image is invalid, that is, the ratio of the overlapping part of the image with the already covered area to the total area of the image. The greater this loss, the smaller the validity of the image.

[0074] Quality grade factor : Define the quality grade factor according to the different quality grades of the images. The optimal choice during coverage is images with quality grades A and B, grade C is an alternative, and grade D should be avoided as much as possible. Among them, for quality grade A: , for quality grade B: , for quality grade C: , for quality grade D: .

[0075] Phase difference loss : Used to evaluate the phase quality of the images within the selected dataset . When selecting images, for low-latitude regions, annual data can be used; for mid-latitude regions, summer data is optimal, followed by spring and autumn data; for high-latitude regions, only summer data should be used as much as possible. In addition, the phase difference of the images within the selected dataset should also be considered to ensure that the phase differences are close.

[0076] The algorithm content is as follows:

[0077] Input:

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

[0079] Dataset ;

[0080] The boundaries of the images (latitude and longitude ranges);

[0081] The satellite models and shooting times of the images (for same-orbit grouping);

[0082] The cloud amount of the images (affecting the revenue value);

[0083] The quality grades of the images (A, B, C, D).

[0084] Output:

[0085] Optimized sub-dataset : The optimal set of images selected to cover the target area .

[0086] The algorithm steps are as follows:

[0087] Step 3.1: Create an image set;

[0088] Traverse the image database and select the ones that match the target area The set of all intersecting images is the data set .

[0089] Step 3.2: Group by shooting task;

[0090] Group the selected images by satellite model and shooting time according to the shooting task (i.e., images in the same orbit), and each group is represented as . The images within each group belong to the same shooting task of the same satellite and have the same or similar timestamps.

[0091] Step 3.3: Calculate the coverage benefit of single-orbit images;

[0092] To calculate the overall coverage benefit of a set of images in a certain orbit, it is necessary to first calculate the coverage benefit of all images within the orbit. For each group for each remote sensing image , according to its quality level factor , cloud cover , shooting time and the overlapping area with the target area, calculate the effective coverage benefit of the remote sensing image , and the calculation formula is as follows:

[0093] ;

[0094] where is the optimal shooting time, is the quality level factor of the remote sensing image , is the repeated coverage loss of the image ;

[0095] Calculate the effective coverage benefit of images in the same orbit The formula is as follows:

[0096] ;

[0097] where is the total number of images within a single orbit, is the shooting time of the single-orbit images, is the repeated coverage loss of the entire orbit. Different from the single-scene calculation, the images within the same orbit will have overlapping at the edges, and the repeated coverage loss of the entire orbit is the ratio of the effective coverage area of the entire orbit to the area of the entire orbit of images.

[0098] Step 3.4: Update the target area;

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

[0100] Step 3.5: Loop calculation;

[0101] In the initial target area After determining the effective coverage benefit of the optimal single-track image It is necessary to ensure that the phase difference between other tracks and is close. The method of the present invention uses the root mean square error to calculate the phase difference. The root mean square error The calculation formula is as follows:

[0102] ;

[0103] Substitute After normalization and substitution into the above formula, we get:

[0104] ;

[0105] Among them, is the imaging time of the subsequent selected single-track image, 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 benefit of each newly added single-track image, it is necessary to consider the imaging times of all the selected single tracks.

[0106] In summary, select the single track with the highest maximum effective coverage benefit multiple times and update the target area multiple times until the uncovered area or , that is, all areas are covered or there are no effective images. Merge the images within all the used tracks into the final target optimization sub-dataset .

[0107] The remote sensing data coverage screening method based on the 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 an image quality control and consistency guarantee mechanism. Compared with the prior art, the method of the present invention can reduce the storage and calculation burden of redundant images, maximize the efficiency of image coverage, and ensure the accuracy and consistency of global surface coverage.

[0108] The remote sensing data coverage screening method based on the benefit value of the present invention assumes that the images have passed precise quality inspections and are marked with accurate quality grades (A, B, C, D) and cloud cover percentages. This pre-calibration method ensures that no additional quality screening steps are required during the image selection process, thus avoiding repeated detection of image quality and significantly improving the data processing efficiency. In addition, accurate cloud cover detection enables more precise exclusion of images with large cloud cover when selecting images, ensuring the clarity of the covered area.

[0109] The method for screening remote sensing data coverage based on revenue value of the present invention optimizes image selection through a greedy algorithm to ensure that all target areas are covered by the most suitable set of images as much as possible, while minimizing image overlap. Compared with the simple coverage model of the prior art, the method of the present invention can reduce the use of redundant images, maximize the coverage efficiency of each area, reduce the waste of computing resources, and improve the benefit of data use.

[0110] The method for screening remote sensing data coverage based on revenue value of the present invention ensures the temporal consistency of images and the consistency of ground colors by preferentially selecting images of the same-orbit tasks. Different from the problems of illumination differences and color inconsistencies between images existing in the prior art, the method of the present invention can ensure a high degree of consistency in hue and illumination of the selected images, thereby providing a more accurate and coherent surface coverage image and ensuring the consistency of image quality and visual effects.

[0111] The method for screening remote sensing data coverage based on revenue value of the present invention adopts a greedy algorithm and an image selection strategy to ensure that only the optimal set of images is selected for each area, avoiding the storage and calculation of redundant images. Compared with the problems of repeated calculation and storage redundancy that may occur in the prior art, the method of the present invention significantly reduces the computing and storage burdens and improves the data processing efficiency and storage utilization rate.

[0112] The method for screening remote sensing data coverage based on revenue value of the present invention provides a dynamic image selection framework, flexibly adjusts the image selection strategy according to the characteristics of different areas (such as latitude, seasonality, satellite orbit, etc.), and ensures the best coverage effect. This flexibility enables the method of the present invention to adapt to the special needs of different geographical areas, provide a more accurate and efficient image selection scheme, and break through the limitations of the static selection method in the prior art.

[0113] Obviously, the above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope 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 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 optimal sub-dataset ; Step 2: Check the data set attributes; Ensure the dataset Each image in has the following attributes: image coverage area vector, image quality, image cloud cover, shooting time and satellite model; Step 3: Area coverage algorithm; 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 a dataset There is no other suitable data.

2. The remote sensing data coverage screening method based on benefit value according to claim 1 is characterized in that: The optimized sub-dataset in step 1 satisfy: Full coverage target ; Optimizing Subdatasets Remote sensing images The repetition rate between them is low. ; 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 benefit 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 image cloudiness is expressed as a value in the range of 0-100%.

4. The remote sensing data coverage screening method based on benefit value according to claim 1, characterized in that: Step 3 The specific steps are: Step 3.1: Create an image collection; Traverse the image database and select the target area The set of all images that intersect is the dataset ; Step 3.2: Group by shooting task; The filtered images are grouped into shooting tasks according to satellite model and shooting time. Each group is represented as ; Step 3.3: Calculate the single-track image coverage gain; 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: ； in, For the best shooting time, For remote sensing images The quality grade factor, For images Loss of duplicate coverage; Calculate the effective coverage gain of the same track image The formula is as follows: ； in, is the total number of images in a single track, is the shooting time of the single track image, Repeat for the entire track to cover the loss; Step 3.4: Update the target area; After selecting the single track with the maximum effective coverage benefit, update the uncovered area , remove the covered part of the selected image; Step 3.5: loop calculation; 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: ； Will Normalize and substitute into the above formula to get: ； 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.

Images