Remote sensing data processing method, system and device

Abstract

The application is suitable for the technical field of remote sensing data processing, and particularly relates to a remote sensing data processing method and application thereof. The remote sensing data processing method comprises the following steps: obtaining remote sensing receiving information; analyzing the remote sensing receiving information to obtain first regional information and second regional information; analyzing the first regional information to obtain first remote sensing data; analyzing the second regional information to obtain second remote sensing data; and obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data. The method can be used for targeted optimization processing of different regions, avoids the one-size-fits-all problem in the traditional method, reduces the consumption of computing resources while ensuring the information quantity, improves the processing efficiency, improves the overall quality of the image, and is also conducive to improving the consistency and integrity of the information of different regions.

Description

Technical Field

[0001] This application belongs to the field of remote sensing data processing technology, and in particular relates to a remote sensing data processing method and its application. Background Technology

[0002] Remote sensing technology is a technique that uses sensors to collect information about the Earth's surface or atmosphere from distant locations (such as satellites, aircraft, and drones). These sensors measure the energy of electromagnetic waves reflected, emitted, or scattered from the Earth's surface and convert it into data that can be analyzed. Remote sensing technology has been widely applied in many fields, such as Geographic Information Systems (GIS), environmental monitoring, disaster management, agriculture, forestry, and urban planning.

[0003] Traditional remote sensing data processing methods typically employ a uniform processing flow, applying the same processing to all data. This method is slow and may result in the loss of information in some areas or poor processing quality, leading to less than ideal remote sensing images. Summary of the Invention

[0004] This application provides a remote sensing data processing method and its application, which can solve the problems of slow processing speed and unsatisfactory remote sensing images obtained by traditional remote sensing data processing methods.

[0005] In a first aspect, embodiments of this application provide a remote sensing data processing method, including: Acquire remote sensing reception information; wherein, the remote sensing reception information is used to reflect the radiation data reflected from the earth's surface to the receiver; Based on the analysis of the remote sensing received information, first area information and second area information are obtained; wherein, the first area information is used to reflect the areas on the observed land surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the areas on the observed land surface where the structural complexity is lower than the preset complexity threshold. Based on the information of the first region, the first remote sensing data is obtained; Based on the analysis of the information from the second region, second remote sensing data is obtained; A remote sensing image is obtained based on the first remote sensing data and the second remote sensing data.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The remote sensing data processing method provided in this application acquires remote sensing reception information reflecting radiation data reflected from the Earth's surface to a receiver. Then, it analyzes this information to obtain first region information reflecting areas on the Earth's surface with structural complexity exceeding a preset complexity threshold, and second region information reflecting areas on the Earth's surface with structural complexity below the preset complexity threshold. The first region information is then analyzed to obtain first remote sensing data; the second region information is analyzed to obtain second remote sensing data; and a remote sensing image is obtained based on the first and second remote sensing data. This method analyzes remote sensing reception information, identifies and distinguishes between the first and second region information, analyzes them separately to obtain different remote sensing data, and finally integrates the different remote sensing data from different regions to obtain a remote sensing image. This allows for targeted optimization of different regions, avoiding the one-size-fits-all problem of traditional methods. While ensuring sufficient information, it reduces computational resource consumption, improves processing efficiency, and not only improves the overall image quality but also enhances the consistency and completeness of information from different regions.

[0007] In one possible implementation of the first aspect, the step of analyzing the remote sensing received information to obtain first area information and second area information includes: Based on the analysis of the remote sensing received information, structural feature information is obtained; wherein, the structural feature information includes the elevation changes and feature boundaries of the observed surface structures; Based on the structural feature information, the information of the first region and the information of the second region are obtained through analysis.

[0008] In one possible implementation of the first aspect, the step of analyzing the structural feature information to obtain first region information and second region information includes: Based on the structural feature information, analysis is performed to obtain first feature information and second feature information; wherein, the first feature information is different from the second feature information; First region information is obtained based on first feature information, and second region information is obtained based on second feature information.

[0009] In one possible implementation of the first aspect, the step of analyzing the structural feature information to obtain the first feature information and the second feature information includes: The curvature features are obtained by analyzing the feature boundaries in the structural feature information; the curvature features are used to reflect the degree of change of the feature boundaries. The curvature feature is compared with a preset curvature to obtain first curvature information and second curvature information; wherein, the first curvature information is used to reflect the structural feature information corresponding to the curvature feature that is greater than or equal to the preset curvature, and the second curvature information is used to reflect the structural feature information corresponding to the curvature feature that is less than the preset curvature; Based on the elevation change, the first curvature information, and the second curvature information in the structural feature information, the first feature information and the second feature information are obtained.

[0010] In one possible implementation of the first aspect, the step of analyzing the elevation change, the first curvature information, and the second curvature information in the structural feature information to obtain the first feature information and the second feature information includes: Based on the elevation changes in the structural feature information, a first complexity feature and a second complexity feature are obtained; wherein, the first complexity feature is used to reflect the structural feature information corresponding to elevation changes greater than or equal to a preset threshold, and the second complexity feature is used to reflect the structural feature information corresponding to elevation changes less than the preset threshold; Calculate the correlation value between the second curvature information and the first complexity feature; wherein the correlation value is used to reflect the degree of correlation between the second curvature information and the first complexity feature; First feature information is obtained based on the first curvature information, the first complexity feature, and the second curvature information corresponding to the association value being greater than or equal to a preset association value; second feature information is obtained based on the second complexity feature and the second curvature information corresponding to the association value being less than the preset association value.

[0011] In one possible implementation of the first aspect, the step of analyzing the first area information to obtain the first remote sensing data includes: First optimized data and second optimized data are obtained based on the first area information; wherein, the first optimized data is used to indicate high-resolution remote sensing data in the remote sensing received information corresponding to the first area information, and the second optimized data is used to indicate low-resolution remote sensing data in the remote sensing received information corresponding to the first area information. The first remote sensing data is obtained by analyzing the first optimized data and the second optimized data.

[0012] In one possible implementation of the first aspect, the step of analyzing the first optimized data and the second optimized data to obtain the first remote sensing data includes: Feature extraction is performed based on the second optimized data to obtain important feature information; wherein, the important feature information is used to reflect the features in the first region information that need to be processed; The important feature information is fused with the first optimized data to obtain the first remote sensing data.

[0013] In one possible implementation of the first aspect, the step of analyzing the second region information to obtain second remote sensing data includes: Based on the second area information, third optimized data is obtained, and the third optimized data is determined as the second remote sensing data; wherein, the third optimized data is used to indicate the low-resolution remote sensing data in the remote sensing received information corresponding to the second area information.

[0014] In one possible implementation of the first aspect, obtaining the remote sensing image based on the first remote sensing data and the second remote sensing data includes: Spatial registration is performed between the first remote sensing data and the second remote sensing data to obtain optimized remote sensing data; Remote sensing images are generated based on the optimized remote sensing data.

[0015] Secondly, embodiments of this application provide a remote sensing data processing system, including: An acquisition module is used to acquire remote sensing received information; wherein, the remote sensing received information is used to reflect the radiation data reflected from the earth's surface to the receiver; The first analysis module is used to analyze the remote sensing received information to obtain first area information and second area information; wherein, the first area information is used to reflect the areas on the observed land surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the areas on the observed land surface where the structural complexity is lower than the preset complexity threshold. The second analysis module is used to analyze the information of the first region to obtain the first remote sensing data; The third analysis module is used to analyze the information of the second region to obtain the second remote sensing data; An integration module is used to obtain a remote sensing image based on the first remote sensing data and the second remote sensing data.

[0016] Thirdly, embodiments of this application provide a remote sensing data processing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any one of the first aspects above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the first aspects above.

[0018] Fifthly, embodiments of this application provide a computer program product that, when run on a remote sensing data processing device, causes the remote sensing data processing device to execute the remote sensing data processing method described in any one of the first aspects.

[0019] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the remote sensing data processing method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the implementation process of step S221 in the remote sensing data processing method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the remote sensing data processing system provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the remote sensing data processing device provided in the embodiments of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determination" or "if the described condition or event is detected" may be interpreted, depending on the context, as "once determination," "in response to determination," "once the described condition or event is detected," or "in response to the detection of the described condition or event."

[0026] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] Remote sensing technology is a technique that uses sensors to collect information about the Earth's surface or atmosphere from distant locations (such as satellites, aircraft, and drones). These sensors measure the energy of electromagnetic waves reflected, emitted, or scattered from the Earth's surface and convert it into data that can be analyzed. Remote sensing technology has been widely applied in many fields, such as Geographic Information Systems (GIS), environmental monitoring, disaster management, agriculture, forestry, and urban planning.

[0029] Traditional remote sensing data processing methods typically employ a uniform processing flow, applying the same processing to all data. This method is slow and may result in the loss of information in some areas or poor processing quality, leading to less than ideal remote sensing images.

[0030] To address the aforementioned problems, this application provides a remote sensing data processing method and its application. In this method, remote sensing reception information reflecting radiation data reflected from the Earth's surface to a receiver is acquired. Then, the remote sensing reception information is analyzed to obtain first region information reflecting areas on the observed Earth's surface with structural complexity exceeding a preset complexity threshold, and second region information reflecting areas on the observed Earth's surface with structural complexity below the preset complexity threshold. The first region information is then analyzed to obtain first remote sensing data; the second region information is analyzed to obtain second remote sensing data; and a remote sensing image is obtained based on the first and second remote sensing data. This method analyzes the remote sensing reception information, identifies and distinguishes between the first and second region information, analyzes them separately to obtain different remote sensing data, and finally integrates the different remote sensing data from different regions to obtain a remote sensing image. This allows for targeted optimization of different regions, avoiding the one-size-fits-all problem of traditional methods. While ensuring sufficient information, it reduces computational resource consumption, improves processing efficiency, and not only improves the overall image quality but also enhances the consistency and completeness of information from different regions.

[0031] The remote sensing data processing method provided in this application embodiment can be applied to a remote sensing data processing device. In this case, the remote sensing data processing device is the executing entity of the remote sensing data processing method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of remote sensing data processing device.

[0032] For example, remote sensing data processing equipment can be mobile phones, tablets, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), desktop computers, smart screens, smart TVs, and other terminal devices; handheld devices with wireless communication capabilities; computing devices or other processing devices connected to wireless modems; IoT terminals; computers; laptops; handheld communication devices; handheld computing devices; satellite wireless devices; wireless modem cards; set-top boxes (STBs); customer premises equipment (CPEs); and / or other devices used for communication over wireless systems, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved Public Land Mobile Networks (PLMNs).

[0033] To better understand the remote sensing data processing method provided in the embodiments of this application, the specific implementation process of the remote sensing data processing method provided in the embodiments of this application will be described by way of example below.

[0034] Figure 1 A schematic flowchart of a remote sensing data processing method provided in an embodiment of this application is shown. The remote sensing data processing method includes: S100, acquire remote sensing reception information; wherein, the remote sensing reception information is used to reflect the radiation data reflected from the earth's surface to the receiver.

[0035] It is understandable that remote sensing information can be received by collecting remote sensing data using sensors on satellites, aircraft, or other platforms. Sensors can be optical (such as multispectral and hyperspectral imagers) or non-optical (such as radar). Remote sensing data can also be received simultaneously by multiple sensors at different spatial scales, i.e., using satellite platforms to obtain large-scale macroscopic information, using aircraft or UAV platforms to obtain high-resolution information of local areas, and so on, but not limited to these.

[0036] S200, based on the remote sensing received information, analyze to obtain first area information and second area information; wherein, the first area information is used to reflect the areas on the observed land surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the areas on the observed land surface where the structural complexity is lower than a preset complexity threshold.

[0037] It is understandable that a preset complexity threshold is a pre-defined value for the level of complexity. This value can be manually input, obtained from a remote sensing database, or otherwise, but is not limited to these methods. The preset complexity threshold can be set through laboratory experiments, past experience, and other means. A remote sensing database refers to a database containing data such as the preset complexity threshold. The data in the remote sensing database can be obtained through laboratory experiments, field measurements and monitoring, and past experience. After acquisition, the collected data is organized, classified, and archived, useful information and patterns are extracted, and the relevant data is then saved into the database to form a remote sensing database.

[0038] Different types of observed objects reflect different amounts of radiation, and even the same type of observed object will reflect different amounts of radiation depending on its geographical location. After acquiring remote sensing reception information, by comparing and analyzing each data point, and by analogy based on the type of received data and the reception time at different spatial scales, all remote sensing data can be divided into first-region information and second-region information. Alternatively, the remote sensing reception information can be directly input into a machine learning model, which will then output first-region information and second-region information, and so on, but not limited to these methods. The machine learning model is trained using a large amount of labeled remote sensing data, which is used to label the first-region information and the second-region information.

[0039] In one possible implementation, please refer to Figure 2 In step S200, the remote sensing received information is analyzed to obtain first area information and second area information, including: S210. Based on the remote sensing received information, structural feature information is obtained through analysis; among which, structural feature information includes the elevation changes of the observed surface structures and the boundaries of ground features.

[0040] Elevation change refers to the variation in the height of the Earth's surface, which can be obtained through analysis using a Digital Elevation Model (DEM). Elevation change reflects the undulation of the terrain. Feature boundaries refer to the boundaries between different features on the Earth's surface, such as the dividing lines between roads, rivers, and buildings. Feature boundaries reflect the shape and distribution of features. DEM data and multispectral data can be extracted from remote sensing reception information. Then, the DEM data can be analyzed, for example, by analyzing the interval between received data and calculating the corresponding elevation changes. Similarly, multispectral data can be analyzed, for example, by analyzing the types of received data or radiation bands, to identify the corresponding observed objects. By analyzing the continuity between observed objects, feature boundaries can be obtained.

[0041] S220, based on the structural feature information, analyze to obtain the information of the first region and the information of the second region.

[0042] For example, structural feature information can be used for feature recognition, and the area can be divided according to the magnitude of elevation changes and the complexity of the feature boundaries. That is, an elevation change threshold and a feature boundary threshold can be set respectively, and then the structural feature information can be compared with these two thresholds to obtain the first region information and the second region information. Alternatively, the structural feature information can be input into a machine learning model, and the machine learning model can output the corresponding first region information and the second region information, and so on, but not limited to these.

[0043] This setup, by obtaining structural feature information, allows for a comprehensive understanding of the complexities of the observed Earth's surface. Based on this structural feature information, information about the first and second regions can be derived, enabling precise identification and efficient classification of surface areas. This method not only improves the accuracy of data processing and analysis but also optimizes data utilization efficiency, supports various application scenarios, and enhances data quality and fusion capabilities.

[0044] In one possible implementation, in step S220, analysis is performed based on structural feature information to obtain first region information and second region information, including: S221, Analyze the structural feature information to obtain the first feature information and the second feature information; wherein the first feature information is different from the second feature information.

[0045] It is understandable that the first feature information is used to reflect areas with higher complexity within the observed surface area. The second feature information is used to reflect areas with simpler complexity within the observed surface area. This can be achieved by separately identifying the elevation changes and feature boundaries in the structural feature information, calculating the comprehensive feature complexity of the elevation changes and feature boundaries for each area, and so on. This comprehensive feature complexity can be obtained by weighting the elevation changes and feature boundaries, or by taking the maximum value of their complexity, etc., and is not limited to these methods. Then, the comprehensive feature complexity is compared and classified to obtain the first and second feature information. Alternatively, the structural feature information can be input into a machine learning model, which then outputs the corresponding first and second feature information, etc., but is not limited to these methods.

[0046] In one possible implementation, please refer to Figure 2 In step S221, analysis is performed based on structural feature information to obtain first feature information and second feature information, including: S2211, based on the analysis of the ground feature boundaries in the structural feature information, the curvature features are obtained; the curvature features are used to reflect the degree of change of the ground feature boundaries.

[0047] It is understandable that curvature features can be calculated on the boundaries of ground features using curvature detection algorithms to obtain curvature values ​​for different ground feature boundaries and curvature values ​​for different regions (i.e., different line segments) of the same ground feature boundary. The larger the curvature value reflected by the curvature feature, the more drastic the change in the boundary line (such as sharp turns or irregular shapes), while a low curvature value indicates a gentler change in the boundary line (such as straight lines or smooth curves).

[0048] S2212, compare the curvature feature with the preset curvature to obtain first curvature information and second curvature information; wherein, the first curvature information is used to reflect the structural feature information corresponding to the curvature feature that is greater than or equal to the preset curvature, and the second curvature information is used to reflect the structural feature information corresponding to the curvature feature that is less than the preset curvature.

[0049] It is understandable that the preset curvature is a pre-defined curvature value, which can be manually input, obtained from a remote sensing database, etc., but is not limited to these methods. After comparing each curvature value in the curvature feature with the preset curvature value, two sets of curvature values ​​are obtained. One set of curvature values ​​is greater than or equal to the preset curvature, and the other set of curvature values ​​is less than the preset curvature. Then, the structural feature information corresponding to the curvature values ​​in the two sets is divided to obtain the first curvature information and the second curvature information.

[0050] S2213, based on the elevation change, first curvature information and second curvature information in the structural feature information, the first feature information and second feature information are obtained.

[0051] For example, the first feature information and the second feature information can be obtained by classifying the elevation change, the first curvature information and the second curvature information in the structural feature information respectively; or the elevation change, the first curvature information and the second curvature information can be weighted and divided to obtain the complexity of the features of each unit region, and then the features of each unit region can be classified as the first feature information or the second feature information, etc., but not limited to these.

[0052] This approach, by comprehensively considering elevation changes and curvature information, allows for a more accurate description of surface features, avoiding the biases that can arise from analyzing a single feature. It also provides quantitative data support for both the primary and secondary feature information. The primary feature information reflects the detailed characteristics of complex areas, while the secondary feature information embodies the basic characteristics of simpler areas. This comprehensive analysis helps provide high-quality surface information, supporting further applications and decision-making.

[0053] In one possible implementation, please refer to Figure 2 In step S2213, the elevation change, first curvature information, and second curvature information in the structural feature information are analyzed to obtain the first feature information and the second feature information, including: S22131, Analyze the elevation changes in the structural feature information to obtain a first complexity feature and a second complexity feature; wherein, the first complexity feature is used to reflect the structural feature information corresponding to elevation changes greater than or equal to a preset threshold, and the second complexity feature is used to reflect the structural feature information corresponding to elevation changes less than a preset threshold.

[0054] It is understandable that the preset threshold is a pre-defined value for the degree of elevation change. It can be manually input by the user or obtained from a remote sensing database, etc., but is not limited to these methods.

[0055] S22132, calculate the correlation value between the second curvature information and the first complexity feature; wherein, the correlation value is used to reflect the degree of correlation between the second curvature information and the first complexity feature.

[0056] It is understandable that the correlation value can be represented by the minimum spatial distance between the surface location of the second curvature information and the surface location of the first complexity feature, or by the co-occurrence frequency between the second curvature information and the first complexity feature, and so on, but is not limited to these. For example, the distance between the edge of a low-curvature region and the edge of the nearest high-complexity region can be calculated. A smaller spatial distance indicates that the low-curvature region may be associated with high-complexity terrain, while a larger spatial distance indicates a weaker correlation. Alternatively, the co-occurrence frequency of low-curvature regions and high-complexity terrain regions can be calculated using statistical correlation coefficients (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.). For example, the correlation between these two features can be reflected by calculating the frequency of the simultaneous existence of low-curvature and high-complexity terrain within a certain range.

[0057] S22133, first feature information is obtained based on first curvature information, first complexity feature, and second curvature information corresponding to a correlation value greater than or equal to a preset correlation value; second feature information is obtained based on second complexity feature and second curvature information corresponding to a correlation value less than a preset correlation value.

[0058] It can be understood that the first feature information includes the first curvature information, the first complexity feature, and the second curvature information corresponding to a correlation value greater than or equal to a preset correlation value, and the second feature information includes the second complexity feature and the second curvature information corresponding to a correlation value less than a preset correlation value.

[0059] This setup, by combining elevation changes, curvature, and correlation values, enables more precise identification of surface feature regions. The method can distinguish between highly complex and low-complexity surface areas in detail, while considering both elevation changes and curvature characteristics. This meticulous classification helps to more accurately describe the complexity of surface features. The multi-dimensional analysis combining elevation changes, curvature information, and correlation values ​​yields efficient and accurate data processing and analysis results, contributing to improved feature recognition, enhanced correlation analysis, and more precise and comprehensive surface feature analysis.

[0060] S222, obtain first region information based on first feature information, and obtain second region information based on second feature information.

[0061] It is understandable that by calculating the ratio of the first feature information to the second feature information, the first feature information or the second feature information with a smaller ratio can be marked first. That is, if the first feature information has a smaller ratio, the first feature information is divided and marked as a memory region, and the other second feature information is marked as the second region information.

[0062] This setup, through structural feature information analysis, yields first and second feature information, which are then transformed into actual regional information. Specifically, a first region and a second region are delineated based on the first and second feature information, respectively. This method improves the accuracy and efficiency of region delineation, supports in-depth terrain analysis, and provides a scientific basis and decision support for various practical applications.

[0063] S300: Based on the information from the first region, the first remote sensing data is obtained.

[0064] It is understandable that the first remote sensing data is used to generate a remote sensing image of the first region. This can be achieved by labeling the first region and then extracting the corresponding remote sensing received information from the received remote sensing data. This received information includes remote sensing data at different spatial scales, such as high-resolution and low-resolution data. The first remote sensing data can be obtained by analyzing the high-resolution and low-resolution data. Alternatively, the high-resolution remote sensing data corresponding to the first region can be directly identified as the first remote sensing data, and so on, but these methods are not limited to these approaches.

[0065] In one possible implementation, in step S300, the first remote sensing data is obtained by analyzing the first region information, including: S310, acquire first optimized data and second optimized data based on the first area information; wherein, the first optimized data is used to indicate high-resolution remote sensing data in the remote sensing received information corresponding to the first area information, and the second optimized data is used to indicate low-resolution remote sensing data in the remote sensing received information corresponding to the first area information.

[0066] For example, after determining the first area information, the first area information can be located and then matched with the remote sensing received information to obtain the corresponding first optimized data and second optimized data; alternatively, the high-resolution remote sensing data and low-resolution remote sensing data in the remote sensing received information can be mapped to the first area information to obtain the corresponding first optimized data and second optimized data, and so on, but not limited to these.

[0067] S320. Based on the analysis of the first optimized data and the second optimized data, the first remote sensing data is obtained.

[0068] For example, the first remote sensing data can be obtained by extracting features from the second optimized data, removing redundant parts from the second optimized data, and then fusing the first optimized data with the processed second optimized data; or the first optimized data and the second optimized data can be weighted, multiplied, and then the multiplied data can be normalized, etc., but not limited to these.

[0069] In one possible implementation, in step S320, the first remote sensing data is obtained by analyzing the first optimized data and the second optimized data, including: S321, feature extraction is performed based on the second optimized data to obtain important feature information; wherein, the important feature information is used to reflect the features in the first region information that need to be processed.

[0070] For example, all important features can be obtained from a remote sensing database and then matched with second optimized data. All important features in the second optimized data that match the remote sensing database are then labeled and summarized to obtain important feature information. Alternatively, the second optimized data can be input into a learning model, which then outputs the corresponding important feature information, and so on, but not limited to these methods. The remote sensing database also includes the corresponding important features of all different land surface structures. The learning model is trained using multiple sets of training data; that is, it is trained by collecting a large amount of low-resolution remote sensing data of the land surface and the corresponding labels of important features.

[0071] S322, the important feature information is fused with the first optimized data to obtain the first remote sensing data.

[0072] For example, the first remote sensing data can be obtained by spatially registering the first optimized data and important feature information, then overlaying them, and normalizing and smoothing the overlaid data; or the feature descriptors of the important feature information can be fused with the feature descriptors of the first optimized data, and the fused data can be further processed, such as normalization and smoothing, to obtain the first remote sensing data, and so on, but not limited to these.

[0073] This setup allows for the fusion of key feature information from the first and second optimized data to obtain the first remote sensing data. This process retains high-resolution detail information while also including key feature information. Furthermore, it enables rapid analysis and acquisition of remote sensing data for the first region, accelerating the analysis process and improving the completeness and accuracy of the remote sensing data.

[0074] S400: Based on the information from the second region, the second remote sensing data is obtained.

[0075] It is understandable that the surface structure reflected by the second area information is relatively simple. After marking the second area information, the corresponding low-resolution remote sensing data can be directly extracted from the remote sensing received information as the second remote sensing data; or the corresponding resolution remote sensing data can be selected as the second remote sensing data according to the size of the area reflected by the second area information, and so on, but not limited to these.

[0076] In one possible implementation, in step S400, the second remote sensing data is obtained by analyzing the second region information, including: The third optimized data is obtained based on the second area information, and the third optimized data is determined as the second remote sensing data; wherein, the third optimized data is used to indicate the low-resolution remote sensing data in the remote sensing reception information corresponding to the second area information.

[0077] It is understandable that by marking the information of the second region, the corresponding low-resolution remote sensing data can be directly extracted from the remote sensing received information as the second remote sensing data.

[0078] S500 obtains remote sensing images based on first and second remote sensing data.

[0079] It is understandable that after obtaining the first remote sensing data corresponding to the first region information and the second remote sensing data corresponding to the second region information, the data can be registered and fused using a spatial registration algorithm to obtain a complete remote sensing image; alternatively, all feature points in the remote sensing received information can be matched with the first and second remote sensing data respectively, so that the first and second remote sensing data are simultaneously mapped into the same space, and then the mapped remote sensing data can be transformed to generate a remote sensing image, and so on, but not limited to these.

[0080] In one possible implementation, in step S500, obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data includes: S510 spatially registers the first and second remote sensing data to obtain optimized remote sensing data.

[0081] For example, the first and second remote sensing data can be preprocessed, including denoising and smoothing, and then feature points can be extracted from the first and second remote sensing data using algorithms such as SIFT and SURF. The feature points in the first and second remote sensing data can be matched to find corresponding points between the first and second remote sensing data. Based on the matched feature points, spatial registration can be performed, and the registered first and second remote sensing data can be merged to generate optimized remote sensing data.

[0082] S520 generates remote sensing images based on optimized remote sensing data.

[0083] For example, the high-resolution and low-resolution portions of the optimized remote sensing data can be fused using a pixel-level fusion method, and then geometric transformation parameters can be calculated to convert the data into a corresponding remote sensing image.

[0084] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0085] Corresponding to the remote sensing data processing method described in the above embodiments, this application also provides a remote sensing data processing system, the various modules of which can implement the various steps of the remote sensing data processing method. Figure 3 A structural block diagram of the remote sensing data processing system provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0086] Reference Figure 3 The remote sensing data processing system includes: The acquisition module is used to acquire remote sensing received information; wherein, the remote sensing received information is used to reflect the radiation data reflected from the earth's surface to the receiver.

[0087] The first analysis module is used to analyze remote sensing received information to obtain first area information and second area information. The first area information is used to reflect areas on the observed surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect areas on the observed surface where the structural complexity is lower than the preset complexity threshold.

[0088] The second analysis module is used to analyze the information from the first region to obtain the first remote sensing data.

[0089] The third analysis module is used to analyze the information from the second region to obtain the second remote sensing data.

[0090] The integration module is used to obtain remote sensing images based on the first and second remote sensing data.

[0091] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0092] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described module division is merely an example. In practical applications, the above functions can be assigned to different modules as needed, that is, the internal structure of the system can be divided into different modules to complete all or part of the functions described above. The modules in the embodiments can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0093] This application also provides a remote sensing data processing device. Figure 4 This is a schematic diagram of the structure of a remote sensing data processing device 6 provided in an embodiment of this application. Figure 4 As shown, the remote sensing data processing device 6 of this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown in the image), at least one memory 61 ( Figure 4 (Only one is shown in the diagram) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60, wherein when the processor 60 executes the computer program 62, it causes the remote sensing data processing device 6 to implement the steps in any of the above-described remote sensing data processing method embodiments, or causes the remote sensing data processing device 6 to implement the functions of each module in the above-described system embodiments.

[0094] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 62 in the remote sensing data processing device 6.

[0095] The remote sensing data processing device 6 can be a desktop computer, laptop, handheld computer, or cloud server, etc. This remote sensing data processing device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 4 This is merely an example of the remote sensing data processing device 6 and does not constitute a limitation on the remote sensing data processing device 6. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0096] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0097] In some embodiments, the memory 61 may be an internal storage unit of the remote sensing data processing device 6, such as a hard disk or memory of the remote sensing data processing device 6. In other embodiments, the memory 61 may be an external storage device of the remote sensing data processing device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the remote sensing data processing device 6. Further, the memory 61 may include both internal and external storage units of the remote sensing data processing device 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0098] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0099] This application provides a computer program product that, when run on a remote sensing data processing device, enables the remote sensing data processing device to implement the steps in any of the above method embodiments.

[0100] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a remote sensing data processing device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0101] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0103] In the embodiments provided in this application, it should be understood that the disclosed remote sensing data processing equipment and system can be implemented in other ways. For example, the remote sensing data processing system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0104] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. Remote sensing data processing methods, including: Acquire remote sensing reception information, which is used to reflect the radiation data reflected from the Earth's surface to the receiver; Based on the analysis of the remote sensing received information, first area information and second area information are obtained. The first area information is used to reflect the areas on the observed land surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the areas on the observed land surface where the structural complexity is lower than the preset complexity threshold. Based on the information of the first region, the first remote sensing data is obtained; Based on the analysis of the information from the second region, second remote sensing data is obtained; A remote sensing image is obtained based on the first remote sensing data and the second remote sensing data; The step of analyzing the remote sensing received information to obtain first area information and second area information includes: Based on the analysis of the remote sensing received information, structural feature information is obtained, which includes the elevation changes of the observed surface structure and the boundaries of ground features. Based on the structural feature information, analysis is performed to obtain first region information and second region information, specifically including: Based on the structural feature information, a first feature information and a second feature information are obtained, wherein the first feature information is different from the second feature information. First region information is obtained based on first feature information, and second region information is obtained based on second feature information; The analysis operation to obtain the first feature information and the second feature information includes: The curvature features are obtained by analyzing the feature boundaries in the structural feature information; the curvature features are used to reflect the degree of change of the feature boundaries. The curvature feature is compared with a preset curvature to obtain first curvature information and second curvature information. The first curvature information is used to reflect the structural feature information corresponding to the curvature feature that is greater than or equal to the preset curvature, and the second curvature information is used to reflect the structural feature information corresponding to the curvature feature that is less than the preset curvature. Based on the elevation change, the first curvature information, and the second curvature information in the structural feature information, the first feature information and the second feature information are obtained through analysis. The analysis based on the elevation change, the first curvature information, and the second curvature information in the structural feature information to obtain the first feature information and the second feature information includes: Based on the elevation changes in the structural feature information, a first complexity feature and a second complexity feature are obtained. The first complexity feature is used to reflect the structural feature information corresponding to elevation changes greater than or equal to a preset threshold, and the second complexity feature is used to reflect the structural feature information corresponding to elevation changes less than the preset threshold. Calculate the correlation value between the second curvature information and the first complexity feature, wherein the correlation value is used to reflect the degree of correlation between the second curvature information and the first complexity feature; First feature information is obtained based on the first curvature information, the first complexity feature, and the second curvature information corresponding to the association value being greater than or equal to a preset association value; second feature information is obtained based on the second complexity feature and the second curvature information corresponding to the association value being less than the preset association value.

2. The method as described in claim 1, characterized in that, The step of analyzing the information of the first region to obtain the first remote sensing data includes: Based on the first area information, first optimized data and second optimized data are obtained. The first optimized data is used to indicate high-resolution remote sensing data in the remote sensing received information corresponding to the first area information, and the second optimized data is used to indicate low-resolution remote sensing data in the remote sensing received information corresponding to the first area information. The first remote sensing data is obtained by analyzing the first optimized data and the second optimized data.

3. The method as described in claim 2 above, characterized in that, The step of analyzing the first optimized data and the second optimized data to obtain the first remote sensing data includes: Based on the second optimized data, feature extraction is performed to obtain important feature information, which is used to reflect the features in the first region information that need to be processed. The important feature information is fused with the first optimized data to obtain the first remote sensing data.

4. The method as described in claim 1, characterized in that, The step of analyzing the information from the second region to obtain the second remote sensing data includes: Based on the second area information, third optimized data is obtained, and the third optimized data is determined as the second remote sensing data. The third optimized data is used to indicate the low-resolution remote sensing data in the remote sensing received information corresponding to the second area information.

5. The method as described in claim 1, characterized in that, The process of obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data includes: Spatial registration is performed between the first remote sensing data and the second remote sensing data to obtain optimized remote sensing data; Remote sensing images are generated based on the optimized remote sensing data.

6. A remote sensing data processing system for implementing the method of claim 1, comprising: An acquisition module is used to acquire remote sensing received information; wherein, the remote sensing received information is used to reflect the radiation data reflected from the earth's surface to the receiver; The first analysis module is used to analyze the remote sensing received information to obtain first area information and second area information; wherein, the first area information is used to reflect the areas on the observed land surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the areas on the observed land surface where the structural complexity is lower than the preset complexity threshold. The second analysis module is used to analyze the information of the first region to obtain the first remote sensing data; The third analysis module is used to analyze the information of the second region to obtain the second remote sensing data; An integration module is used to obtain a remote sensing image based on the first remote sensing data and the second remote sensing data.

7. A remote sensing data processing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of claims 1-5.

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