Multi-spectral remote sensing image strip radiation consistency correction method and system

Abstract

The invention provides a multispectral remote sensing image strip radiation consistency correction method and system. The method comprises the following steps: acquiring a plurality of remote sensing image strips with a lateral or heading overlapping relationship; constructing a graph structure based on the coverage area of the plurality of remote sensing image strips; for each connecting edge in the graph structure, determining an attribute value of the connecting edge based on an invariant pixel identified from the strip overlapping region corresponding to the connecting edge; for each node, fitting a radiation inconsistency equation between adjacent nodes based on an invariant pixel to obtain an attribute value of the node; iteratively adjusting radiation correction parameters of each image strip by adopting a greedy optimization algorithm on the basis of attribute values of nodes and connecting edges in the graph structure so as to determine a radiation correction parameter set; and based on the radiation correction parameter set, performing radiation correction on each image strip in sequence to generate a multispectral remote sensing image strip with consistent radiation. According to the invention, correction of multispectral remote sensing image stripe radiation consistency can be realized based on a graph optimization mode.

Description

Technical Field

[0001] The present application relates to the field of remote sensing satellite technology, and in particular to a method and system for correcting the radiation consistency of multispectral remote sensing image stripes. Background Art

[0002] With the rapid development of remote sensing technology, multispectral imaging is playing an increasingly important role in environmental monitoring, agricultural management, disaster response, and other fields. High-spatial-resolution multispectral imagery not only accurately captures high-resolution surface features but also effectively reflects the spectral characteristics of ground objects, providing important technical support for precise analysis and decision-making. However, due to multiple factors such as sensor performance limitations, changing atmospheric conditions, and differences in observation geometry, the radiometric response of multispectral imagery often exhibits significant inconsistencies in both temporal and spatial dimensions. This radiometric inconsistency not only severely impacts the reliability and accuracy of image data but also adversely affects the effectiveness of key tasks such as crop growth monitoring, dynamic disaster assessment, and precise decision support. Therefore, acquiring multispectral imagery with temporal and spatial radiometric consistency is crucial for ensuring the scientific and practical application of remote sensing data in production applications.

[0003] To address the radiometric inconsistency problem of multispectral imagery, researchers have proposed a variety of correction methods, including statistical model-based radiometric correction techniques, image registration strip smoothing methods, and machine learning-driven radiometric consistency optimization techniques. However, these methods generally have the following limitations: First, they are often highly dependent on external standard reference data (such as Landsat or Sentinel-2 imagery), or require the designation of an internal reference standard as a benchmark. This not only increases dependence on external resources (e.g., only spectral bands compatible with the reference data can be corrected), but also limits the scope of application of the correction method, especially when the reference data does not cover the target area or time period. Second, most existing methods fail to fully consider the inherent interrelationships between multispectral image strips and are unable to perform overall correction of multi-phase, multi-band imagery. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for correcting the radiation consistency of multispectral remote sensing image strips, which can realize the correction of the radiation consistency of multispectral remote sensing image strips based on graph optimization, and is used to eliminate the radiation inconsistency errors between multiple multispectral image strips with lateral or heading overlapping relationships, without relying on external standard reference data. At the same time, it can also fully consider the inherent relationships between multispectral image strips and perform overall correction on multi-phase and multi-band images.

[0005] In a first aspect, the present application provides a method for correcting the radiation consistency of multispectral remote sensing image strips, the method comprising: obtaining multiple remote sensing image strips with lateral or heading overlapping relationships; constructing a graph structure based on the coverage of the multiple remote sensing image strips; in the graph structure, each remote sensing image strip is regarded as a node, and when there is an overlapping area between two remote sensing image strips, a connecting edge is established between the two nodes; for each connecting edge in the graph structure, based on the invariant pixels identified from the strip overlapping area corresponding to the connecting edge, the attribute value of the connecting edge is determined; for each node in the graph structure, the radiation inconsistency equation between adjacent nodes is fitted based on the invariant pixels to obtain the attribute value of the node; based on the attribute values of the nodes and connecting edges in the graph structure, a greedy optimization algorithm is used to iteratively adjust the radiation correction parameters of each image strip to determine a set of radiation correction parameters; based on the set of radiation correction parameters, each image strip is radiation corrected in turn to generate a multispectral remote sensing image strip with consistent radiation.

[0006] Furthermore, the step of determining the attribute value of the connecting edge based on the invariant pixels identified from the strip overlap region corresponding to the connecting edge includes: identifying the invariant pixels in the strip overlap region corresponding to the connecting edge according to a multivariate change detection method; and calculating the radiometric inconsistency error between adjacent nodes corresponding to the connecting edge using the identified invariant pixels according to the following first specified formula: ,in, Represents adjacent node strips With strips The radiation inconsistency error, and Represents the first The surface reflectance value of the unchanged pixel, is the total number of unchanged pixels in the overlapping area of the two belts; The radiation inconsistency error between adjacent nodes is used as the attribute value of the connecting edge.

[0007] Furthermore, the above step of fitting the radiation inconsistency equation between adjacent nodes based on the invariant pixel to obtain the attribute value of the node includes: and the corresponding connection Connect nodes to fit the radiation inconsistency equation and get the set , as a node The property set of ; where the radiation inconsistency equation is as follows: ; in, and Represents adjacent node strips With strips The surface reflectance values of all unchanged pixels in the overlapping area, is the slope, is the intercept, is the residual; The attribute set of each node is determined as the attribute value of the node.

[0008] Furthermore, the above-mentioned step of iteratively adjusting the radiation correction parameters of each image strip based on the attribute values of the nodes and the connecting edges in the graph structure and determining the radiation correction parameter set by using a greedy optimization algorithm includes: for the current round of iteration, performing the following calculation steps: calculating the overall radiation inconsistency error of the graph structure under the current round of iteration and the radiation inconsistency error of each node according to the attribute values of each connecting edge; calculating the correction amount of the correction parameter of each node according to the attribute value of each node; determining the new correction parameter of each node after correction based on the correction amount of the correction parameter of each node; calculating the radiation inconsistency error of each node after applying the new correction parameter; and calculating the radiation inconsistency error of each node after applying the new correction parameter. Inconsistency error, and radiation inconsistency error of each node, calculate the correctable error of each node; determine the target node corresponding to the maximum correctable error, adjust the radiation correction parameter of the target node to the corresponding new correction parameter, update the attribute values of the nodes and edges associated with the target node after the parameter adjustment, and update the overall radiation inconsistency error of the graph structure based on the updated attribute values; determine whether the difference between the overall radiation inconsistency error of the updated graph structure and the overall radiation inconsistency error of the graph structure under the current iteration is greater than a threshold; if so, continue to execute the calculation steps under the next iteration; if not, stop the iteration, and the radiation correction parameters of each node constitute a radiation correction parameter set.

[0009] Furthermore, the step of calculating the overall radiation inconsistency error of the graph structure under the current iteration and the radiation inconsistency error of each node according to the attribute values of each connecting edge includes: calculating the overall radiation inconsistency error of the graph structure under the current iteration and the radiation inconsistency error of each node according to the following two formulas: ; ; in, The overall radiation inconsistency error representing the graph structure; Representation node Radiation inconsistency error; N and M are the total number of nodes and connecting edges in the graph structure; is the number of nodes connected to node n; For strips With its The radiation inconsistency error between adjacent strips.

[0010] Furthermore, the step of calculating the correction amount of the calibration parameter of each node according to the attribute value of each node includes: calculating the average attribute value of the node based on the attribute value of each node according to the following formula: and ; in, Is with the node The number of connected nodes; and For nodes The average value of the attribute; Calculate the correction parameter of each node according to the following formula: and ; in, and is a node Correction parameter correction.

[0011] Furthermore, the step of determining the corrected new correction parameters of each node based on the correction amount of the correction parameters of each node includes: calculating the corrected new correction parameters of each node according to the following formula: and ; in, and are the current radiation correction parameters, with initial values of 1 and 0 respectively; and For nodes Correction parameter correction; and For nodes The new calibration parameters after correction.

[0012] Furthermore, the step of calculating the correctable error of each node based on the radiation inconsistency error of each node after applying the new correction parameters and the radiation inconsistency error of each node includes: calculating the correctable error of each node according to the following formula: ; in, Representation node Correctable error; For nodes The level of radiometric inconsistency error after applying the current correction factor, For nodes Radiometric inconsistency error after applying the new correction parameters.

[0013] Furthermore, the above-mentioned step of sequentially performing radiometric correction on each image strip based on the radiometric correction parameter set to generate radiometrically consistent multispectral remote sensing image strips includes: using a product reflectance image provided by a third party as a correction reference image, and systematically fine-tuning the radiometric correction parameters in the radiometric correction parameter set to obtain optimized radiometric correction parameters; the product reflectance image provided by the third party includes a reflectance image from Landsat-8 or Sentinel-2; and sequentially performing radiometric correction on each image strip according to the following formula to generate radiometrically consistent multispectral remote sensing image strips: ,in, is the surface reflectance image strip after radiation consistency correction, is the surface reflectance image strip before correction, and are the optimized radiation correction parameters respectively.

[0014] In the second aspect, the present application also provides a multispectral remote sensing image strip radiation consistency correction system, the system including: a remote sensing image acquisition module, used to acquire multiple remote sensing image strips with lateral or heading overlapping relationships; a graph structure construction module, used to construct a graph structure based on the coverage of multiple remote sensing image strips; in the graph structure, each remote sensing image strip is regarded as a node, and when there is an overlapping area between two remote sensing image strips, a connecting edge is established between the two nodes; an attribute value determination module, used to determine the attribute value of each connecting edge in the graph structure based on the invariant pixels identified from the strip overlapping area corresponding to the connecting edge; for each node in the graph structure, the radiation inconsistency equation between adjacent nodes is fitted based on the invariant pixels to obtain the attribute value of the node; a correction parameter determination module, used to iteratively adjust the radiation correction parameters of each image strip based on the attribute values of the nodes and connecting edges in the graph structure using a greedy optimization algorithm to determine a set of radiation correction parameters; a radiation correction module, used to perform radiation correction on each image strip in turn based on the radiation correction parameter set to generate multispectral remote sensing image strips with consistent radiation.

[0015] In the multispectral remote sensing image strip radiation consistency correction method and system provided by the present application, first, multiple remote sensing image strips with lateral or heading overlapping relationships are obtained; then, a graph structure is constructed based on the coverage of the multiple remote sensing image strips; in the graph structure, each remote sensing image strip is regarded as a node, and when there is an overlapping area between two remote sensing image strips, a connecting edge is established between the two nodes; for each connecting edge in the graph structure, based on the invariant pixels identified from the strip overlapping area corresponding to the connecting edge, the attribute value of the connecting edge is determined; for each node in the graph structure, the radiation inconsistency equation between adjacent nodes is fitted based on the invariant pixels to obtain the attribute value of the node; finally, based on the attribute values of the nodes and connecting edges in the graph structure, a greedy optimization algorithm is used to iteratively adjust the radiation correction parameters of each image strip to determine a radiation correction parameter set; and based on the radiation correction parameter set, each image strip is radiation corrected in turn to generate multispectral remote sensing image strips with consistent radiation. This application can realize the correction of the radiation consistency of multispectral remote sensing image strips based on graph optimization, which is used to eliminate the radiation inconsistency errors between multiple multispectral image strips with lateral or heading overlapping relationships, without relying on external standard reference data. At the same time, it can also fully consider the inherent relationships between multispectral image strips and perform overall correction on multi-phase and multi-band images. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 A flowchart of a method for correcting the radiation consistency of multispectral remote sensing image strips provided in an embodiment of the present application; Figure 2 A schematic diagram of a module flow provided in an embodiment of the present application; Figure 3 A schematic diagram of a comparison result provided in an embodiment of the present application; Figure 4 A schematic diagram of another comparison result provided in an embodiment of the present application; Figure 5 This is a structural block diagram of a multispectral remote sensing image strip radiation consistency correction system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions of this application in conjunction with the embodiments. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] In view of the problems in the existing methods for correcting the radiation inconsistency of multispectral images (such as radiation correction technology based on statistical models, strip smoothing methods for image registration, and radiation consistency optimization technology driven by machine learning), such as high dependence on external standard reference data, small scope of application of the correction methods, and failure to fully consider the inherent relationships between multispectral image strips, the embodiments of the present application provide a multispectral remote sensing image strip radiation consistency correction method and system, which can realize the correction of the radiation consistency of multispectral remote sensing image strips based on graph optimization, and is used to eliminate the radiation inconsistency errors between multiple multispectral image strips with lateral or heading overlapping relationships, without relying on external standard reference data. At the same time, it can also fully consider the inherent relationships between multispectral image strips and perform overall correction on multi-phase and multi-band images.

[0020] To facilitate understanding of this embodiment, a multispectral remote sensing image strip radiation consistency correction method disclosed in an embodiment of the present application is first introduced in detail. A graph is an ideal structure that can effectively represent the mutual radiation relationship of internal images. Thanks to the flexibility and extensibility of the graph structure, graph-based optimization can achieve overall and real-time correction of radiation inconsistency errors without relying on any reference data. Therefore, the embodiment of the present application provides a multispectral remote sensing image strip radiation consistency correction method and system based on graph optimization, which has important research value and broad application prospects.

[0021] Figure 1 This is a flowchart of a method for correcting the radiometric consistency of multispectral remote sensing image strips provided in an embodiment of the present application. The method specifically includes the following steps: Step S102 : Acquire a plurality of remote sensing image strips having a lateral or directional overlapping relationship; that is, a plurality of PlanetScope (PS) image strips.

[0022] Step S104: constructing a graph structure based on the coverage of the multiple remote sensing image strips; in the graph structure, each remote sensing image strip is regarded as a node, and when there is an overlapping area between two remote sensing image strips, a connecting edge is established between the two nodes; In this step, in order to organize the image strips into a whole, when constructing the graph structure, each remote sensing image strip is defined as a node in the graph structure. If there is an overlapping area between two strips, a connecting edge is established between the corresponding nodes. In this embodiment, only the connecting edges in which the effective observation ratio in the overlapping area exceeds 50% and the acquisition time interval between the two PS image strips does not exceed 2 days can be retained. Since one strip corresponds to one sensor when the remote sensing image is collected, in this embodiment, the image strip data with an interval of no more than two days is set to construct the graph structure, which can ensure that the reflectivity of the ground objects observed by different sensors remains unchanged. Only the data with an effective observation ratio of more than 50% in the overlapping area is retained, which is also to ensure that the reflectivity of the remote sensing image is as consistent as possible, thereby improving the accuracy of subsequent data analysis.

[0023] Step S106: For each edge in the graph structure, the attribute value of the edge is determined based on the invariant pixels identified from the overlapping regions of the strips corresponding to the edge; for each node in the graph structure, the radiation inconsistency equation between adjacent nodes is fitted based on the invariant pixels to obtain the attribute value of the node; Step S108 , based on the attribute values of the nodes and connecting edges in the graph structure, a greedy optimization algorithm is used to iteratively adjust the radiation correction parameters of each image strip to determine a radiation correction parameter set; The specific implementation process of the algorithm can be found in the detailed description in the following embodiments.

[0024] Step S110 : Based on the radiation correction parameter set, each image strip is sequentially subjected to radiation correction to generate a multispectral remote sensing image strip with consistent radiation.

[0025] In a preferred embodiment, a small amount of data can be used to fine-tune the radiation correction parameters in the radiation correction parameter set to ensure that the corrected image strips are consistent with the reflectivity product provided by a third party in terms of radiation consistency.

[0026] For example, using a third-party product reflectance image as a correction reference image, systematically fine-tune the radiometric correction parameters in the radiometric correction parameter set to obtain optimized radiometric correction parameters; the third-party product reflectance image includes the reflectance image of Landsat-8 or Sentinel-2. Then, perform radiometric correction on each image strip in turn according to the following formula to generate radiometrically consistent multispectral remote sensing image strips: ,in, is the surface reflectance image strip after radiation consistency correction, is the surface reflectance image strip before correction, and are the optimized radiation correction parameters respectively.

[0027] It should be noted that the above embodiment is a preferred method. If parameter fine-tuning is not performed, each image strip can also be directly corrected in sequence using the radiation correction parameters in the radiation correction parameter set. In this case, and They respectively represent the radiation correction parameters in the radiation correction parameter set.

[0028] The multispectral remote sensing image strip radiation consistency correction method provided in the embodiment of the present application can realize the radiation consistency correction of multispectral remote sensing image strips based on graph optimization, and is used to eliminate the radiation inconsistency error between multiple multispectral image strips with lateral or heading overlapping relationships. It does not need to rely on external standard reference data, and can also fully consider the inherent relationship between multispectral image strips to perform overall correction on multi-phase and multi-band images.

[0029] An embodiment of the present application also provides another method for correcting the radiation consistency of multispectral remote sensing image strips, which is implemented on the basis of the above embodiment; this embodiment focuses on describing the process of determining the attribute values of nodes and connecting edges in the graph structure, and the process of determining the radiation correction parameter set using a greedy optimization algorithm.

[0030] The step of "determining the attribute value of the connecting edge based on the invariant pixels identified from the strip overlap area corresponding to the connecting edge" in the above step S106 includes: (1) Using the Multivariate Alteration Detection (MAD) method, the unchanged pixels in the overlapping areas of the strips corresponding to the connecting edges are identified; (2) According to the first specified formula below, the radiation inconsistency error between adjacent nodes corresponding to the connecting edge is calculated using the identified invariant pixels: ,in, Represents adjacent node strips With strips The radiation inconsistency error, and Represents the first The surface reflectance value of the unchanged pixel, is the total number of unchanged pixels in the overlapping area of the two belts; (3) The radiation inconsistency error between adjacent nodes is used as the attribute value of the connecting edge.

[0031] Furthermore, the step of "fitting the radiation inconsistency equation between adjacent nodes based on the invariant pixels to obtain the attribute value of the node" in the above step S106 includes: (1) For nodes and the corresponding connection The radiometric inconsistency equation (RIE) is fitted to the connected nodes to obtain the set , as a node The property set of ; where the radiation inconsistency equation is as follows: ; in, and Represents adjacent node strips With strips The surface reflectance values of all unchanged pixels in the overlapping area are expressed in the radiation inconsistency equation as follows: is the slope, is the intercept, and All as nodes The attribute value of , that is, node The attribute set of is the residual; (2) The attribute set of each node is determined as the attribute value of the node.

[0032] Furthermore, the step of "iteratively adjusting the radiation correction parameters of each image strip using a greedy optimization algorithm based on the attribute values of the nodes and connecting edges in the graph structure to determine the radiation correction parameter set" in step S108 includes: For the current iteration, the following calculation steps are performed: (1) According to the attribute values of each connecting edge, calculate the overall radiation inconsistency error of the graph structure under the current iteration and the radiation inconsistency error of each node; In specific implementation, the overall radiation inconsistency error of the structure shown below and the radiation inconsistency error of each node in the current round of iteration are calculated according to the following two formulas: ; ; in, The overall radiation inconsistency error representing the graph structure; Representation node Radiation inconsistency error; N and M are the total number of nodes and connecting edges in the graph structure; is the number of nodes connected to node n; For strips With its The radiation inconsistency error between adjacent strips.

[0033] (2) Calculate the correction parameter of each node based on the attribute value of each node; In specific implementation, the attribute average of the node is calculated based on the attribute value of each node according to the following formula: and ; in, and Node Attribute value of Is with the node The number of connected nodes; and For nodes The average value of the attribute; Calculate the correction parameter of each node according to the following formula: and ; in, and is a node Correction parameter correction.

[0034] (3) Based on the correction amount of the correction parameters of each node, determine the new correction parameters of each node after correction; In specific implementation, the new correction parameters of each node after correction are calculated according to the following formula: and ; in, and are the current radiation correction parameters, with initial values of 1 and 0 respectively; and For nodes Correction parameter correction; and For nodes The new calibration parameters after correction.

[0035] (4) Calculate the radiation inconsistency error of each node after applying the new correction parameters; (5) Calculate the correctable error of each node based on the radiation inconsistency error of each node after applying the new correction parameters and the radiation inconsistency error of each node; In specific implementation, the correctable error of each node is calculated according to the following formula: ; in, Representation node Correctable error; For nodes The level of radiometric inconsistency error after applying the current correction factor, For nodes Radiometric inconsistency error after applying the new correction parameters.

[0036] (6) Determine the target node corresponding to the maximum correctable error, adjust the radiation correction parameters of the target node to the corresponding new correction parameters, update the attribute values of the nodes and edges associated with the target node after the parameter adjustment, and update the overall radiation inconsistency error of the graph structure based on the updated attribute values; (7) Determine whether the difference between the overall radiation inconsistency error of the updated graph structure and the overall radiation inconsistency error of the graph structure in the current iteration is greater than a threshold; if so, continue to execute the calculation steps in the next iteration; if not, stop the iteration, and form a radiation correction parameter set based on the radiation correction parameters of each node.

[0037] In the embodiment of the present application, the node n with a higher radiation inconsistency error level is preferentially selected for correction, and the correction parameter of the node is updated. and , and node attributes , the edge attributes connected to the node , the radiation inconsistency error level of the node , and simultaneously update the overall radiation inconsistency error level of the graph Repeat step 5 until the global radiation inconsistency error level does not decrease significantly during the iteration process, and then determine the optimal correction parameter set. .

[0038] See also Figure 2 In one embodiment shown, a multispectral remote sensing image strip radiation consistency correction device includes the following modules: S1. Graph Building Blocks S1-1. Graph structure building blocks Data input: Coverage of PS image strips.

[0039] Data output: The image strips are regarded as nodes in the graph structure, and all image strips are organized into a complete graph structure based on the lateral or heading overlap relationship between the image strips.

[0040] S1-2. Invariant pixel recognition module Data input: graph structure output by S1-1.

[0041] Data output: Identify the invariant pixels in the overlapping area of the two overlapping strips corresponding to each edge in the graph structure.

[0042] S1-3. Graph attribute calculation module Data input: unchanged pixels identified by S1-2.

[0043] Data output: Generate a graph structure with complete connection edges and node attributes.

[0044] S2. Graph Optimization Module S2-1. Graph Optimization Module Based on Greedy Strategy Data input: Complete graph output by S1.

[0045] Data output: Optimize the graph structure through a greedy strategy to generate the corresponding strip image radiation correction parameters.

[0046] S2-2. Third-party reflectivity product fine-tuning Data input: radiation correction parameters output by S2-1.

[0047] Data output: Using Landsat-8 and Sentinel-2 reflectance products as reference standards, fine-tuned radiometric correction parameters are generated.

[0048] S2-3. Image-by-image banding correction Data input: initial calibration parameters output by the S2-1 unit, or fine-tuned calibration parameters output by the S2-2 unit.

[0049] Data output: Generate strip images with completed radiation correction.

[0050] In another embodiment, Figure 3 The figure shows the comparison results of the PS strip image of the study area (a river basin) before and after radiometric uniformity correction in the embodiment of this application. The radiometric uniformity correction method proposed in this embodiment is used to compare the original image, the image corrected by the official algorithm of the relevant company, and the image corrected by the method provided in this embodiment. The results are shown in Figure 2. Figure 4 As shown. Among them, the radiometric inconsistency between image strips corrected by the method provided by this embodiment has been completely eliminated, further demonstrating the high reliability and significant performance advantages of the method provided by this embodiment. As shown in Table 1, the method provided by this embodiment can not only comprehensively correct the radiometric inconsistency errors of all spectral bands in all original image strips, but also achieve the optimal correction effect, as manifested by the lowest mean absolute error (MAE) and mean relative error (MRE). Table 1 shows the radiometric consistency correction accuracy table for this study area using the method of this embodiment, the original strips, and the official consistency method of the relevant company (bold indicates the optimal score).

[0051] Table 1

[0052] The present application also provides a method for correcting the radiation consistency of multispectral remote sensing image strips. First, multiple remote sensing image strips with lateral or heading overlap are obtained; a graph structure is constructed based on the coverage of the strips, and the image strips are defined as nodes in the graph structure, and the overlapping areas are defined as edges connecting the nodes. In each edge, the invariant pixels in the overlapping area are identified, and the radiation inconsistency errors of adjacent nodes are calculated based on these invariant pixels as the attribute values of the edge; for each node, the radiation inconsistency equation between adjacent nodes is fitted using the invariant pixels, and the fitting coefficient is set as the node attribute. On this basis, based on the attribute values of the nodes and edges, a greedy optimization algorithm is used to iteratively optimize the radiation parameters of the image strips to gradually reduce the global radiation inconsistency error. Ultimately, the user can choose to fine-tune the correction parameters with a small amount of data to make the corrected image consistent with the third-party reflectance product; or directly use the optimized correction parameters to correct the image strips one by one to achieve overall radiation consistency correction. This method significantly improves the radiation consistency of remote sensing images and provides high-quality data assurance for image analysis and application.

[0053] Based on the above method embodiment, the present application embodiment also provides a multispectral remote sensing image strip radiation consistency correction system, see Figure 5 As shown, the system includes: a remote sensing image acquisition module 502, which is used to acquire multiple remote sensing image strips with lateral or heading overlapping relationships; a graph structure construction module 504, which is used to construct a graph structure based on the coverage of the multiple remote sensing image strips; in the graph structure, each remote sensing image strip is regarded as a node, and when there is an overlapping area between two remote sensing image strips, a connecting edge is established between the two nodes; an attribute value determination module 506, which is used to determine the attribute value of each connecting edge in the graph structure based on the invariant pixels identified from the strip overlapping area corresponding to the connecting edge; for each node in the graph structure, the radiation inconsistency equation between adjacent nodes is fitted based on the invariant pixels to obtain the attribute value of the node; a correction parameter determination module 508, which is used to iteratively adjust the radiation correction parameters of each image strip based on the attribute values of the nodes and connecting edges in the graph structure using a greedy optimization algorithm to determine a radiation correction parameter set; and a radiation correction module 510, which is used to perform radiation correction on each image strip in turn based on the radiation correction parameter set to generate a multispectral remote sensing image strip with consistent radiation.

[0054] Furthermore, the attribute value determination module 506 is configured to identify invariant pixels in the overlapping region of the strips corresponding to the connecting edge according to a multivariate change detection method; and calculate the radiometric inconsistency error between adjacent nodes corresponding to the connecting edge using the identified invariant pixels according to the following first specified formula: ,in, Represents adjacent node strips With strips The radiation inconsistency error, and Represents the first The surface reflectance value of the unchanged pixel, is the total number of unchanged pixels in the overlapping area of the two belts; The radiation inconsistency error between adjacent nodes is used as the attribute value of the connecting edge.

[0055] Furthermore, the attribute value determination module 506 is used to determine the node and the corresponding connection Connect nodes to fit the radiation inconsistency equation and get the set , as a node The property set of ; where the radiation inconsistency equation is as follows: ; in, and Represents adjacent node strips With strips The surface reflectance values of all unchanged pixels in the overlapping area, is the slope, is the intercept, is the residual; The attribute set of each node is determined as the attribute value of the node.

[0056] Furthermore, the correction parameter determination module 508 is configured to perform the following calculation steps for the current iteration: calculating the overall radiation inconsistency error of the graph structure under the current iteration and the radiation inconsistency error of each node based on the attribute values of each connecting edge; calculating the correction amount of the correction parameter of each node based on the attribute value of each node; determining the new correction parameter of each node after correction based on the correction amount of the correction parameter of each node; calculating the radiation inconsistency error of each node after applying the new correction parameter; and based on the radiation inconsistency error of each node after applying the new correction parameter and the radiation inconsistency error of each node, Calculate the correctable error of each node; determine the target node corresponding to the maximum correctable error, adjust the radiation correction parameters of the target node to the corresponding new correction parameters, update the attribute values of the nodes and edges associated with the target node after the parameter adjustment, and update the overall radiation inconsistency error of the graph structure based on the updated attribute values; determine whether the difference between the overall radiation inconsistency error of the updated graph structure and the overall radiation inconsistency error of the graph structure under the current iteration is greater than a threshold; if so, continue to execute the calculation steps under the next iteration; if not, stop the iteration, and the radiation correction parameters of each node constitute a radiation correction parameter set.

[0057] Furthermore, the correction parameter determination module 508 is used to calculate the overall radiation inconsistency error and the radiation inconsistency error of each node of the structure in the current iteration according to the following two formulas: ; ; in, The overall radiation inconsistency error representing the graph structure; Representation node Radiation inconsistency error; N and M are the total number of nodes and connecting edges in the graph structure; is the number of nodes connected to node n; For strips With its The radiation inconsistency error between adjacent strips.

[0058] Furthermore, the correction parameter determination module 508 is configured to calculate the average attribute value of the node based on the attribute value of each node according to the following formula: and ; in, Is with the node The number of connected nodes; and For nodes The average value of the attribute; Calculate the correction parameter of each node according to the following formula: and ; in, and is a node Correction parameter correction.

[0059] Furthermore, the correction parameter determination module 508 is used to calculate the new correction parameter of each node after correction according to the following formula: and ; in, and are the current radiation correction parameters, with initial values of 1 and 0 respectively; and For nodes Correction parameter correction; and For nodes The new calibration parameters after correction.

[0060] Furthermore, the correction parameter determination module 508 is used to calculate the correctable error of each node according to the following formula: ; in, Representation node Correctable error; For nodes The level of radiometric inconsistency error after applying the current correction factor, For nodes Radiometric inconsistency error after applying the new correction parameters.

[0061] Furthermore, the radiometric correction module 510 is configured to systematically fine-tune the radiometric correction parameters in the radiometric correction parameter set using a third-party provided reflectance image as a correction reference image to obtain optimized radiometric correction parameters. The third-party provided reflectance image includes a Landsat-8 or Sentinel-2 reflectance image. Radiometric correction is performed sequentially on each image strip according to the following formula to generate radiometrically consistent multispectral remote sensing image strips: ,in, is the surface reflectance image strip after radiation consistency correction, is the surface reflectance image strip before correction, and are the optimized radiation correction parameters respectively.

[0062] The system provided in the embodiment of the present application has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the embodiment of the system, reference can be made to the corresponding content in the aforementioned method embodiment.

[0063] An embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the above-mentioned method. The specific implementation can be found in the above-mentioned method embodiment, which will not be repeated here.

[0064] The computer program products of the methods, devices, and electronic devices provided in the embodiments of the present application include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0065] Unless otherwise specifically stated, the relative steps, numerical expressions and values of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0066] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0067] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0068] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. These modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims.

Claims

1. A method for correcting the radiation consistency of multispectral remote sensing image strips, characterized in that: The method comprises: Acquire multiple remote sensing image strips with lateral or heading overlapping relationships; A graph structure is constructed based on the coverage of the plurality of remote sensing image strips; in the graph structure, each remote sensing image strip serves as a node, and when there is an overlapping area between two remote sensing image strips, a connecting edge is established between the two nodes; For each connecting edge in the graph structure, an attribute value of the connecting edge is determined based on invariant pixels identified from the strip overlap region corresponding to the connecting edge; for each node in the graph structure, a radiation inconsistency equation between adjacent nodes is fitted based on the invariant pixels to obtain the attribute value of the node; Based on the attribute values of the nodes and the connecting edges in the graph structure, a greedy optimization algorithm is used to iteratively adjust the radiation correction parameters of each image strip to determine a radiation correction parameter set; Based on the radiation correction parameter set, radiation correction is performed on each image strip in turn to generate multispectral remote sensing image strips with consistent radiation.

2. The method according to claim 1, characterized in that The step of determining the attribute value of the connecting edge based on the invariant pixels identified from the strip overlap area corresponding to the connecting edge comprises: identifying, based on a multivariate change detection method, invariant pixels in the overlapping regions of the strips corresponding to the connecting edges; The radiation inconsistency error between adjacent nodes corresponding to the connecting edge is calculated using the identified invariant pixels according to the following first specified formula: ,in, Represents adjacent node strips With strips The radiation inconsistency error, and Represents the first The surface reflectance value of the unchanged pixel, is the total number of unchanged pixels in the overlapping area of the two belts; The radiation inconsistency error between the adjacent nodes is used as the attribute value of the connecting edge.

3. The method according to claim 1, characterized in that The steps of fitting the radiation inconsistency equation between adjacent nodes based on the invariant pixels to obtain the attribute value of the node include: For Node and the corresponding connection Connect nodes to fit the radiation inconsistency equation and get the set , as a node The property set of ; wherein the radiation inconsistency equation is as follows: ; in, and Represents adjacent node strips With strips The surface reflectance values of all unchanged pixels in the overlapping area, is the slope, is the intercept, is the residual; The attribute set of each node is determined as the attribute value of the node.

4. The method according to claim 3, characterized in that The steps of iteratively adjusting the radiation correction parameters of each image strip using a greedy optimization algorithm based on the attribute values of the nodes and the connecting edges in the graph structure to determine the radiation correction parameter set include: For the current iteration, the following calculation steps are performed: According to the attribute values of each connecting edge, the overall radiation inconsistency error of the graph structure under the current iteration and the radiation inconsistency error of each node are calculated; Calculate the correction parameter of each node according to the attribute value of each node; Based on the correction amount of the correction parameters of each node, determine the new correction parameters of each node after correction; Calculating the radiation inconsistency error of each node after applying the new correction parameters; Calculating a correctable error of each node based on the radiation inconsistency error of each node after applying the new correction parameter and the radiation inconsistency error of each node; Determining a target node corresponding to the maximum correctable error, adjusting the radiation correction parameters of the target node to the corresponding new correction parameters, updating the attribute values of the nodes and edges associated with the target node after the parameter adjustment, and updating the overall radiation inconsistency error of the graph structure based on the updated attribute values; Determine whether a difference between the overall radiation inconsistency error of the updated graph structure and the overall radiation inconsistency error of the graph structure in the current iteration is greater than a threshold; If yes, continue to execute the calculation steps under the next iteration; If not, the iteration is stopped, and the radiation correction parameters of each node form a radiation correction parameter set.

5. The method according to claim 4, characterized in that The steps of calculating the overall radiation inconsistency error of the graph structure below the current iteration and the radiation inconsistency error of each node according to the attribute values of each connecting edge include: According to the following two formulas, calculate the overall radiation inconsistency error and the radiation inconsistency error of each node in the current iteration of the structure shown below: ； ； in, The overall radiation inconsistency error representing the graph structure; Representation node Radiation inconsistency error; N and M are the total number of nodes and connecting edges in the graph structure; is the number of nodes connected to node n; For strips With its The radiation inconsistency error between adjacent strips.

6. The method according to claim 4, characterized in that The steps of calculating the correction amount of the calibration parameters of each node according to the attribute value of each node include: Calculate the average value of the node's attributes based on the attribute value of each node according to the following formula: and ; in, Is with the node The number of connected nodes; and For nodes The average value of the attribute; Calculate the correction parameter of each node according to the following formula: and ; in, and is a node Correction parameter correction.

7. The method according to claim 4, characterized in that The step of determining the corrected new correction parameters of each node based on the correction amount of the correction parameters of each node includes: Calculate the new correction parameters of each node after correction according to the following formula: and ; in, and are the current radiation correction parameters, with initial values of 1 and 0 respectively; and For nodes Correction parameter correction; and For nodes The new calibration parameters after correction.

8. The method according to claim 4, characterized in that The step of calculating the correctable error of each node based on the radiation inconsistency error of each node after applying the new correction parameter and the radiation inconsistency error of each node includes: Calculate the correctable error of each node according to the following formula: ； in, Representation node Correctable error; For nodes The level of radiometric inconsistency error after applying the current correction factor, For nodes Radiometric inconsistency error after applying the new correction parameters.

9. The method according to claim 1, characterized in that The step of performing radiometric correction on each image strip in sequence based on the radiometric correction parameter set to generate radiometrically consistent multispectral remote sensing image strips includes: Using a reflectance image product provided by a third party as a correction reference image, systematically fine-tuning the radiometric correction parameters in the radiometric correction parameter set to obtain optimized radiometric correction parameters; the reflectance image product provided by the third party includes a reflectance image from Landsat-8 or Sentinel-2; According to the following formula, each image strip is radiometrically corrected to generate a multispectral remote sensing image strip with consistent radiometric quality: ,in, is the surface reflectance image strip after radiation consistency correction, is the surface reflectance image strip before correction, and are the optimized radiation correction parameters respectively.

10. A multispectral remote sensing image strip radiation consistency correction system, characterized by: The system comprises: A remote sensing image acquisition module is used to acquire multiple remote sensing image strips with lateral or heading overlapping relationships; A graph structure construction module is used to construct a graph structure based on the coverage of the plurality of remote sensing image strips; in the graph structure, each remote sensing image strip is regarded as a node, and when there is an overlapping area between two remote sensing image strips, a connecting edge is established between the two nodes; an attribute value determination module configured to determine, for each connecting edge in the graph structure, an attribute value of the connecting edge based on invariant pixels identified from the strip overlap region corresponding to the connecting edge; and, for each node in the graph structure, to obtain an attribute value of the node by fitting a radiation inconsistency equation between adjacent nodes based on the invariant pixels; a correction parameter determination module, configured to iteratively adjust the radiation correction parameters of each image strip using a greedy optimization algorithm based on the attribute values of the nodes and connecting edges in the graph structure to determine a radiation correction parameter set; The radiation correction module is used to perform radiation correction on each image strip in turn based on the radiation correction parameter set to generate multispectral remote sensing image strips with consistent radiation.

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