Remote sensing data processing methods and their applications

Abstract

The present application is applicable to the field of remote sensing data processing technology, and in particular to a remote sensing data processing method and its application, wherein the remote sensing data processing method comprises: obtaining remote sensing reception information; analyzing the remote sensing reception information to obtain first area information and second area information; analyzing the first area information to obtain first remote sensing data; analyzing the second area 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 optimize and process different areas in a targeted manner, avoiding the one-size-fits-all problem in traditional methods, while ensuring the amount of information, reducing the consumption of computing resources, improving processing efficiency, and not only improving the overall quality of the image, but also facilitating the improvement of the consistency and integrity of information in different areas.

Description

Technical Field

[0001] The present application belongs to the technical field of remote sensing data processing, and in particular, relates to a remote sensing data processing method and its application. Background Art

[0002] Remote sensing technology is a technology that collects information about the earth's surface or atmosphere from a distance (such as satellites, aircraft, drones and other platforms) through sensors. These sensors can measure the electromagnetic wave energy reflected, emitted or scattered from the surface and convert it into data for analysis. Remote sensing technology has been widely used in many fields, such as geographic information systems (GIS), environmental monitoring, disaster management, agriculture, forestry, urban planning, etc.

[0003] Traditional remote sensing data processing methods usually adopt a unified processing flow and perform the same processing on all data. This method has a slow processing speed and may cause information loss in some areas or poor processing results, resulting in less than ideal remote sensing images. Summary of the invention

[0004] The embodiments of the present application provide a remote sensing data processing method and application thereof, which can solve the problems that the traditional remote sensing data processing method has a slow processing speed and the obtained remote sensing images are not ideal.

[0005] In a first aspect, an embodiment of the present application provides a remote sensing data processing method, comprising:

[0006] Acquiring remote sensing reception information; wherein the remote sensing reception information is used to reflect the radiation data reflected from the surface to the receiver;

[0007] 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 area on the observed surface whose structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed surface whose structural complexity is lower than the preset complexity threshold;

[0008] Analyze the first area information to obtain first remote sensing data;

[0009] Analyze the second area information to obtain second remote sensing data;

[0010] A remote sensing image is obtained based on the first remote sensing data and the second remote sensing data.

[0011] The above technical solutions in the embodiments of the present application have at least the following technical effects:

[0012] The remote sensing data processing method provided in the present application obtains remote sensing receiving information for reflecting radiation data reflected from the surface to the receiver; then analyzes the remote sensing receiving information to obtain first area information for reflecting the area with a structural complexity higher than a preset complexity threshold on the observed surface and second area information for reflecting the area with a structural complexity lower than the preset complexity threshold on the observed surface; then analyzes the first area information to obtain first remote sensing data; analyzes the second area information to obtain second remote sensing data; and obtains a remote sensing image based on the first remote sensing data and the second remote sensing data. The method analyzes the remote sensing receiving information, identifies and distinguishes the first area information and the second area information, analyzes the first area information and the second area information respectively to obtain different remote sensing data, and finally integrates different remote sensing data from different areas to obtain a remote sensing image, which can optimize and process different areas in a targeted manner, avoids the one-size-fits-all problem in traditional methods, reduces the consumption of computing resources while ensuring the amount of information, improves processing efficiency, and not only improves the overall quality of the image, but also helps to improve the consistency and integrity of information in different areas.

[0013] In a possible implementation manner of the first aspect, analyzing according to the remote sensing received information to obtain the first area information and the second area information includes:

[0014] Analyzing the remote sensing received information to obtain structural feature information; wherein the structural feature information includes the elevation change of the structure of the observed surface and the boundary of the ground object;

[0015] The first region information and the second region information are obtained by analyzing the structural feature information.

[0016] In a possible implementation manner of the first aspect, the performing analysis according to the structural feature information to obtain the first region information and the second region information includes:

[0017] Analyze the structural feature information to obtain first feature information and second feature information; wherein the first feature information is different from the second feature information;

[0018] The first region information is obtained based on the first characteristic information, and the second region information is obtained based on the second characteristic information.

[0019] In a possible implementation manner of the first aspect, the performing analysis according to the structural feature information to obtain the first feature information and the second feature information includes:

[0020] Analyzing the feature boundary in the structural feature information to obtain a curvature feature; the curvature feature is used to reflect the degree of change of the feature boundary;

[0021] Comparing the curvature feature 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;

[0022] The first characteristic information and the second characteristic information are obtained by analyzing the elevation change, the first curvature information and the second curvature information in the structural characteristic information.

[0023] In a possible implementation manner of the first aspect, the 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:

[0024] Analyzing the elevation change 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 the elevation change greater than or equal to a preset threshold, and the second complexity feature is used to reflect the structural feature information corresponding to the elevation change less than the preset threshold;

[0025] Calculating a 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;

[0026] The 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 the preset association value; the 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.

[0027] In a possible implementation manner of the first aspect, the performing analysis according to the first area information to obtain the first remote sensing data includes:

[0028] Acquire first optimization data and second optimization data based on the first area information; wherein the first optimization data is used to indicate high-resolution remote sensing data in the remote sensing reception information corresponding to the first area information, and the second optimization data is used to indicate low-resolution remote sensing data in the remote sensing reception information corresponding to the first area information;

[0029] The first optimized data and the second optimized data are analyzed to obtain first remote sensing data.

[0030] In a possible implementation manner of the first aspect, the performing analysis according to the first optimization data and the second optimization data to obtain the first remote sensing data includes:

[0031] Extracting features according to the second optimized data to obtain important feature information; wherein the important feature information is used to reflect the features that need to be processed in the first region information;

[0032] The important feature information is fused with the first optimized data to obtain the first remote sensing data.

[0033] In a possible implementation manner of the first aspect, the performing analysis according to the second area information to obtain the second remote sensing data includes:

[0034] Acquire third optimized data based on the second area information, and determine the third optimized data as second remote sensing data; wherein the third optimized data is used to indicate low-resolution remote sensing data in the remote sensing reception information corresponding to the second area information.

[0035] In a possible implementation manner of the first aspect, obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data includes:

[0036] Performing spatial registration on the first remote sensing data and the second remote sensing data to obtain optimized remote sensing data;

[0037] A remote sensing image is generated based on the optimized remote sensing data.

[0038] In a second aspect, an embodiment of the present application provides a remote sensing data processing system, including:

[0039] An acquisition module, used to acquire remote sensing reception information; wherein the remote sensing reception information is used to reflect the radiation data reflected from the surface to the receiver;

[0040] A 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 area on the observed surface whose structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed surface whose structural complexity is lower than the preset complexity threshold;

[0041] A second analysis module, used for analyzing the first area information to obtain first remote sensing data;

[0042] A third analysis module, used for analyzing the second area information to obtain second remote sensing data;

[0043] An integration module is used to obtain a remote sensing image based on the first remote sensing data and the second remote sensing data.

[0044] In a third aspect, an embodiment of the present application provides 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 implements any of the methods described in the first aspect when executing the computer program.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above-mentioned first aspects is implemented.

[0046] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a remote sensing data processing device, the remote sensing data processing device executes the remote sensing data processing method described in any one of the above-mentioned first aspects.

[0047] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 is a flowchart of a remote sensing data processing method provided in an embodiment of the present application;

[0050] Figure 2 is a schematic diagram of the implementation flow of step S221 in the remote sensing data processing method provided in an embodiment of the present application;

[0051] Figure 3 is a schematic diagram of the structure of a remote sensing data processing system provided in an embodiment of the present application;

[0052] Figure 4 It is a structural diagram of the remote sensing data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0054] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0055] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0056] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0057] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0058] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0059] Remote sensing technology is a technology that collects information about the earth's surface or atmosphere from a distance (such as satellites, aircraft, drones and other platforms) through sensors. These sensors can measure the electromagnetic wave energy reflected, emitted or scattered from the surface and convert it into data for analysis. Remote sensing technology has been widely used in many fields, such as geographic information systems (GIS), environmental monitoring, disaster management, agriculture, forestry, urban planning, etc.

[0060] Traditional remote sensing data processing methods usually adopt a unified processing flow and perform the same processing on all data. This method has a slow processing speed and may cause information loss in some areas or poor processing results, resulting in less than ideal remote sensing images.

[0061] To solve the above problems, the embodiment of the present application provides a remote sensing data processing method and its application. In the method, remote sensing reception information for reflecting radiation data reflected from the surface to the receiver is obtained; then, the remote sensing reception information is analyzed to obtain first area information for reflecting the area where the structural complexity of the observed surface is higher than the preset complexity threshold and second area information for reflecting the area where the structural complexity of the observed surface is lower than the preset complexity threshold; then, the first area information is analyzed to obtain first remote sensing data; the second area information is analyzed to obtain second remote sensing data; and a remote sensing image is obtained based on the first remote sensing data and the second remote sensing data. The method analyzes the remote sensing reception information, identifies and distinguishes the first area information and the second area information, analyzes the first area information and the second area information respectively to obtain different remote sensing data, and finally integrates different remote sensing data of different areas to obtain a remote sensing image, which can optimize and process different areas in a targeted manner, avoid the one-size-fits-all problem in traditional methods, reduce the consumption of computing resources while ensuring the amount of information, improve processing efficiency, and not only improve the overall quality of the image, but also help to improve the consistency and integrity of information in different areas.

[0062] The remote sensing data processing method provided in the embodiment of the present application can be applied to a remote sensing data processing device. In this case, the remote sensing data processing device is the executor of the remote sensing data processing method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the remote sensing data processing device.

[0063] For example, the remote sensing data processing device can be on a mobile phone, a tablet computer, a wearable device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a smart screen, a smart TV and other terminal devices, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, an Internet of Things terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a TV set top box (STB), a customer premises equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, for example, a mobile terminal in a 5G network or a mobile terminal in a future evolved public land mobile network (PLMN).

[0064] In order to better understand the remote sensing data processing method provided in the embodiment of the present application, the specific implementation process of the remote sensing data processing method provided in the embodiment of the present application is exemplarily introduced below.

[0065] Figure 1 A schematic flow chart of a remote sensing data processing method provided in an embodiment of the present application is shown, and the remote sensing data processing method includes:

[0066] S100, acquiring remote sensing reception information; wherein the remote sensing reception information is used to reflect the radiation data reflected from the surface to the receiver.

[0067] It is understood that remote sensing information can be collected by 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, that is, using satellite platforms to obtain large-scale macro information, using aircraft or drone platforms to obtain high-resolution information of local areas, etc., but not limited to this.

[0068] S200, analyzing the remote sensing received information to obtain first area information and second area information; wherein the first area information is used to reflect the area on the observed surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed surface where the structural complexity is lower than the preset complexity threshold.

[0069] It can be understood that the preset complexity threshold is a pre-set complexity value, which can be manually input by humans or obtained from a remote sensing database, etc., but is not limited thereto. The preset complexity threshold can be set by laboratory experiments, past experience, etc. A remote sensing database refers to a database containing data such as preset complexity thresholds. The data in the remote sensing database can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After the data is obtained, the collected data is sorted, classified, and archived, useful information and rules are extracted, and the relevant data is saved in the database to form a remote sensing database.

[0070] Different types of observed objects reflect different radiation data, and the same type of observed objects reflect different radiation data at different geographical locations. After obtaining remote sensing reception information, by comparing and analyzing each data, and by analogy division based on the type of received data and the reception time on a spatial scale, all remote sensing data can be divided into first region information and second region information; remote sensing reception information can also be directly input into a machine learning model, and the machine learning model then outputs first region information and second region information, etc., but not limited to this. The machine learning model is trained using a large amount of labeled remote sensing data, and the first region information and second region information are labeled using labeled remote sensing data.

[0071] In one possible implementation, see Figure 2 In step S200, the first area information and the second area information are obtained by analyzing the remote sensing received information, including:

[0072] S210, analyzing the remote sensing received information to obtain structural feature information; wherein the structural feature information includes the elevation change of the structure of the observed surface and the boundary of the ground object.

[0073] It can be understood that elevation change refers to the change in the height of the surface, which can be obtained through digital elevation model (DEM) analysis. Elevation change reflects the undulation of the terrain. Feature boundaries refer to the boundaries between different features on the surface, such as the dividing lines between roads, rivers, buildings, etc. Feature boundaries reflect the shape and distribution of features. DEM data and multispectral data can be extracted from remote sensing reception information, and then the DEM data can be analyzed, such as analyzing the interval time of receiving data, analyzing and calculating the time interval to obtain the corresponding elevation change, and then the multispectral data can be analyzed, such as analyzing the type or radiation band of the received data to obtain the corresponding observed object. By analyzing the continuous relationship between the observed objects, the feature boundary can be obtained.

[0074] S220, analyzing the structural feature information to obtain first region information and second region information.

[0075] Exemplarily, feature recognition can be performed on the structural feature information, and the area can be divided according to the elevation change size and the complexity of the boundary of the object. That is, an elevation change threshold and a boundary threshold can be set respectively, and then the structural feature information can be compared with the two thresholds to distinguish the first area information and the second area information; the structural feature information can also be input into a machine learning model, and the machine learning model outputs the corresponding first area information and second area information, and so on, but not limited to this.

[0076] In this way, by obtaining structural feature information, the complex situation of the observed surface can be grasped, and the first area information and the second area information can be obtained according to the structural feature information, so as to realize accurate identification and efficient classification of the surface area. This method not only improves the accuracy of data processing and analysis, but also optimizes data utilization efficiency, supports multiple application scenarios, and enhances data quality and fusion capabilities.

[0077] In a possible implementation, in step S220, analyzing the structural feature information to obtain the first region information and the second region information includes:

[0078] S221, analyzing the structural feature information to obtain first feature information and second feature information; wherein the first feature information is different from the second feature information.

[0079] It can be understood that the first feature information is used to reflect the area with higher complexity in the observed surface range. The second feature information is used to reflect the relatively simple area in the observed surface range. The comprehensive feature complexity of the elevation change and the boundary of the object in each area can be calculated by performing feature recognition on the elevation change and the boundary of the object in the structural feature information respectively. The comprehensive feature complexity can be obtained by weighting the elevation change and the boundary of the object, or by taking the maximum value of the complexity of the elevation change and the boundary of the object, etc., but not limited to this, and then the comprehensive feature complexity is compared and classified to obtain the first feature information and the second feature information; the structural feature information can also be input into the machine learning model, and the machine learning model outputs the corresponding first feature information and second feature information, etc., but not limited to this.

[0080] In one possible implementation, see Figure 2 In step S221, the first feature information and the second feature information are obtained by analyzing the structural feature information, including:

[0081] S2211, analyzing the feature boundary in the structural feature information to obtain a curvature feature; the curvature feature is used to reflect the degree of change of the feature boundary.

[0082] It can be understood that the curvature feature can be used to calculate the boundary of the feature through the curvature detection algorithm to obtain the curvature values ​​of different feature boundaries and the curvature values ​​of different regions (i.e., different line segments) of the same feature boundary. The larger the curvature value reflected by the curvature feature, the more dramatic the change of the boundary line (such as a sharp turn or an irregular shape), while the lower the curvature value, the more gentle the change of the boundary line (such as a straight line segment or a smooth curve).

[0083] 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.

[0084] It can be understood that the preset curvature is a preset curvature value, which can be manually input or obtained from a remote sensing database, etc., but is not limited thereto. After comparing each curvature value in the curvature feature with the preset curvature value, two groups of curvature value groups are obtained, one of which has a curvature value greater than or equal to the preset curvature, and the other has a curvature value less than the preset curvature. Then, the structural feature information corresponding to the curvature values ​​in the two groups is divided to obtain the first curvature information and the second curvature information.

[0085] S2213: Analyze 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.

[0086] Exemplarily, 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; the elevation change, the first curvature information and the second curvature information can also be weighted to comprehensively obtain the complexity of the features of each unit area, and then the features of each unit area can be classified as the first feature information or the second feature information, etc., but not limited to this.

[0087] This setting, by comprehensively considering elevation changes and curvature information, can more accurately describe the surface characteristics, avoid the deviation that may be caused by single feature analysis, and provide quantitative data support for the first feature information and the second feature information. The first feature information reflects the detailed characteristics of complex areas, while the second feature information reflects the basic characteristics of simple areas. This comprehensive analysis helps to provide high-quality surface information and support further applications and decision-making.

[0088] In one possible implementation, see Figure 2 In step S2213, the first characteristic information and the second characteristic information are obtained by analyzing the elevation change, the first curvature information and the second curvature information in the structural characteristic information, including:

[0089] 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 the 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 the elevation changes less than the preset threshold.

[0090] It can be understood that the preset threshold is a preset elevation change value, which can be manually input by humans or obtained from a remote sensing database, etc., but is not limited thereto.

[0091] S22132, calculating a 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.

[0092] It can be understood that the correlation value can be represented by the minimum spatial distance between the surface position where the second curvature information is located and the surface position where the first complexity feature is located, or by the co-occurrence frequency between the second curvature information and the first complexity feature, etc., but is not limited to this. Exemplarily, by calculating the distance between the edge of the low curvature area and the edge of the nearest high complexity area. A smaller spatial distance indicates that the low curvature area may be associated with high complexity terrain, and a larger spatial distance indicates that the correlation is weaker. Or by statistical correlation coefficients (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.), the co-occurrence frequency of low curvature areas and high complexity terrain areas is statistically calculated. For example, by calculating the frequency of low curvature and high complexity terrain coexisting within a certain range, the correlation between these two features can be reflected.

[0093] S22133, obtain the first feature information based on the first curvature information, the first complexity feature and the second curvature information corresponding to the associated value being greater than or equal to the preset associated value; obtain the second feature information based on the second complexity feature and the second curvature information corresponding to the associated value being less than the preset associated value.

[0094] It can be understood that the first characteristic information includes the first curvature information, the first complexity characteristic and the second curvature information corresponding to the association value greater than or equal to the preset association value, and the second characteristic information includes the second complexity characteristic and the second curvature information corresponding to the association value less than the preset association value.

[0095] With this setup, surface feature areas can be identified more accurately by combining elevation changes, curvature, and correlation values. This method can distinguish high-complexity and low-complexity surface areas in detail, taking into account both elevation changes and curvature characteristics. This detailed classification helps describe the complexity of surface features more accurately. The multi-dimensional analysis that combines elevation changes, curvature information, and correlation values ​​brings efficient and accurate data processing and analysis results, which helps to improve feature identification, enhance correlation analysis, and achieve more accurate and comprehensive surface feature analysis.

[0096] S222: Obtain first region information based on the first characteristic information, and obtain second region information based on the second characteristic information.

[0097] It can be understood that by calculating the ratio of the first characteristic information to the second characteristic information, the first characteristic information or the second characteristic information with a smaller ratio can be marked preferentially, that is, if the first characteristic information accounts for a smaller ratio, the first characteristic information memory area is divided and marked, and the other second characteristic information is marked as second area information.

[0098] In this way, the first characteristic information and the second characteristic information are obtained by analyzing the structural characteristic information, and the first characteristic information and the second characteristic information are converted into actual regional information, that is, the first region and the second region are respectively divided based on the first characteristic information and the second characteristic information. This method improves the accuracy and efficiency of regional division, supports in-depth terrain analysis, and provides a scientific basis and decision support for various practical applications.

[0099] S300: Analyze the first area information to obtain first remote sensing data.

[0100] It can be understood that the first remote sensing data is used to generate a remote sensing image of the first regional information. The first regional information can be marked and then the remote sensing reception information corresponding to the first regional information can be extracted from the remote sensing reception information, wherein the remote sensing reception information corresponding to the first regional information includes remote sensing data at different spatial scales, that is, high-resolution remote sensing data and low-resolution remote sensing data. The first remote sensing data can be obtained by analyzing the high-resolution remote sensing data and the low-resolution remote sensing data; the high-resolution remote sensing data corresponding to the first regional information can also be directly determined as the first remote sensing data, and so on, but not limited to this.

[0101] In a possible implementation, in step S300, analyzing the first area information to obtain the first remote sensing data includes:

[0102] S310, obtaining first optimization data and second optimization data based on the first area information; wherein the first optimization data is used to indicate high-resolution remote sensing data in the remote sensing reception information corresponding to the first area information, and the second optimization data is used to indicate low-resolution remote sensing data in the remote sensing reception information corresponding to the first area information.

[0103] Exemplarily, after determining the first area information, the first area information can be located and then matched with the remote sensing reception information to obtain corresponding first optimization data and second optimization data; the high-resolution remote sensing data and low-resolution remote sensing data in the remote sensing reception information can also be mapped to the first area information to obtain corresponding first optimization data and second optimization data, and so on, but not limited to this.

[0104] S320, analyzing the first optimized data and the second optimized data to obtain first remote sensing data.

[0105] Exemplarily, 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; the first remote sensing data can also be obtained by weighting the first optimized data and the second optimized data, multiplying them, and then normalizing the multiplied data, etc., but not limited to this.

[0106] In a possible implementation, in step S320, analyzing the first optimization data and the second optimization data to obtain the first remote sensing data includes:

[0107] S321, extracting features according to the second optimized data to obtain important feature information; wherein the important feature information is used to reflect features that need to be processed in the first region information.

[0108] Exemplarily, all important features can be obtained from the remote sensing database, and then matched with the second optimized data, and all important features of the second optimized data that match the remote sensing database are marked and summarized to obtain important feature information; the second optimized data can also be input into the learning model, and the learning model outputs the corresponding important feature information, etc., but not limited to this. The remote sensing database also includes the corresponding important features of all different surface structures. The learning model is trained with multiple sets of training data, that is, the learning model is trained by collecting a large amount of low-resolution remote sensing data of the surface and the identification of the corresponding important features.

[0109] S322, fusing the important feature information with the first optimized data to obtain first remote sensing data.

[0110] Exemplarily, the first optimized data and important feature information can be spatially aligned and then superimposed, and the superimposed data can be normalized and smoothed to obtain the first remote sensing data; the feature descriptors of the important feature information can also 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, etc., but not limited to this.

[0111] With such a setting, the first remote sensing data is obtained by fusing the important feature information in the first optimized data with the second optimized data, which retains the high-resolution detail information and includes the key feature information. At the same time, the remote sensing data of the first area information can be quickly analyzed and acquired, which speeds up the analysis process and improves the completeness and accuracy of the remote sensing data.

[0112] S400: Analyze the second area information to obtain second remote sensing data.

[0113] It can be understood 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 reception information as the second remote sensing data; corresponding resolution remote sensing data can also be selected as the second remote sensing data based on the area size reflected by the second area information, and so on, but not limited to this.

[0114] In a possible implementation, in step S400, analyzing the second area information to obtain the second remote sensing data includes:

[0115] The third optimized data is acquired 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.

[0116] It can be understood that after marking the second region information, the corresponding low-resolution remote sensing data can be directly extracted from the remote sensing reception information as the second remote sensing data.

[0117] S500: Obtain a remote sensing image based on the first remote sensing data and the second remote sensing data.

[0118] It can be understood that after obtaining the first remote sensing data corresponding to the first area information and the second remote sensing data corresponding to the second area information, they can be aligned and fused through a spatial alignment algorithm to obtain a complete remote sensing image; or all feature points in the remote sensing received information can be matched with the first remote sensing data and the second remote sensing data respectively, so that the first remote sensing data and the second remote sensing data are mapped to the same space at the same time, and then the mapped remote sensing data are converted to generate a remote sensing image, etc., but not limited to this.

[0119] In a possible implementation, in step S500, obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data includes:

[0120] S510, spatially registering the first remote sensing data and the second remote sensing data to obtain optimized remote sensing data.

[0121] Exemplarily, the first remote sensing data and the second remote sensing data can be preprocessed, including denoising and smoothing, and then feature points can be extracted from the first remote sensing data and the second remote sensing data using algorithms such as SIFT and SURF. The feature points in the first remote sensing data and the second remote sensing data can be matched, and corresponding points between the first remote sensing data and the second remote sensing data can be found through feature point matching. Spatial alignment can be performed based on the matched feature points, and the aligned first remote sensing data and the second remote sensing data can be merged to generate optimized remote sensing data.

[0122] S520, generating a remote sensing image based on the optimized remote sensing data.

[0123] For example, the high-resolution part and the low-resolution part in the optimized remote sensing data can be fused by using a pixel-level fusion method, and then the geometric transformation parameters are calculated to convert the data into a corresponding remote sensing image.

[0124] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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 the present application.

[0125] Corresponding to the remote sensing data processing method described in the above embodiment, the embodiment of the present application also provides a remote sensing data processing system, and each module of the system can implement each step of the remote sensing data processing method. Figure 3 A structural block diagram of a remote sensing data processing system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0126] Reference Figure 3 , the remote sensing data processing system comprises:

[0127] The acquisition module is used to acquire remote sensing reception information; wherein the remote sensing reception information is used to reflect the radiation data reflected from the surface to the receiver.

[0128] 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 area on the observed surface where the structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed surface where the structural complexity is lower than the preset complexity threshold.

[0129] The second analysis module is used to perform analysis based on the first area information to obtain first remote sensing data.

[0130] The third analysis module is used to perform analysis based on the second area information to obtain second remote sensing data.

[0131] The integration module is used to obtain a remote sensing image based on the first remote sensing data and the second remote sensing data.

[0132] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0133] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above modules is used as an example for illustration. 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 embodiment can be integrated into a processing unit, or each module can exist physically alone, or two or more modules can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the modules are only for the convenience of distinguishing from each other, and are not used to limit the scope of protection of this application. The specific working process of the modules in the above system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0134] The present 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 one embodiment of the present 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), at least one memory 61 ( Figure 4 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the remote sensing data processing device 6 implements the steps in any of the above-mentioned remote sensing data processing method embodiments, or the remote sensing data processing device 6 implements the functions of the modules in the above-mentioned system embodiments.

[0135] Exemplarily, 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 the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the remote sensing data processing device 6.

[0136] The remote sensing data processing device 6 can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The remote sensing data processing device can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 4 It is only an example of the remote sensing data processing device 6 and does not constitute a limitation of the remote sensing data processing device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0137] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0138] 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 also be an external storage device of the remote sensing data processing device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), etc. equipped on the remote sensing data processing device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the remote sensing data processing device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0139] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0140] An embodiment of the present application provides a computer program product. When the computer program product runs on a remote sensing data processing device, the remote sensing data processing device implements the steps in any of the above method embodiments.

[0141] If the integrated unit is implemented in the form of 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, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the remote sensing data processing equipment, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0142] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0143] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] In the embodiments provided in the present 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 only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0145] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A remote sensing data processing method, It is characterized in that include: Acquiring remote sensing reception information; wherein the remote sensing reception information is used to reflect the radiation data reflected from the surface to the receiver; 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 area on the observed surface whose structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed surface whose structural complexity is lower than the preset complexity threshold; Analyze the first area information to obtain first remote sensing data; Analyze the second area information to obtain second remote sensing data; Obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data; The analyzing the remote sensing received information to obtain the first area information and the second area information includes: Analyzing the remote sensing received information to obtain structural feature information; wherein the structural feature information includes the elevation change of the structure of the observed surface and the boundary of the ground object; Analyze the structural feature information to obtain first region information and second region information; The analyzing according to the structural feature information to obtain the first region information and the second region information includes: Analyze the structural feature information to obtain first feature information and second feature information; wherein the first feature information is different from the second feature information; Obtain first region information based on the first feature information, and obtain second region information based on the second feature information; The step of analyzing the structural feature information to obtain the first feature information and the second feature information includes: Analyzing the feature boundary in the structural feature information to obtain a curvature feature; the curvature feature is used to reflect the degree of change of the feature boundary; Comparing the curvature feature 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; Analyze the elevation change, the first curvature information, and the second curvature information in the structural feature information to obtain first feature information and second feature information; 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: Analyzing the elevation change 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 the elevation change greater than or equal to a preset threshold, and the second complexity feature is used to reflect the structural feature information corresponding to the elevation change less than the preset threshold; Calculating a 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; The 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 the preset association value; the 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; The step of analyzing the first area information to obtain first remote sensing data includes: Acquire first optimization data and second optimization data based on the first area information; wherein the first optimization data is used to indicate high-resolution remote sensing data in the remote sensing reception information corresponding to the first area information, and the second optimization data is used to indicate low-resolution remote sensing data in the remote sensing reception information corresponding to the first area information; Analyze the first optimized data and the second optimized data to obtain first remote sensing data; The step of analyzing the first optimized data and the second optimized data to obtain first remote sensing data includes: Extracting features according to the second optimized data to obtain important feature information; wherein the important feature information is used to reflect the features that need to be processed in the first region information; Fusion of the important feature information with the first optimized data to obtain first remote sensing data; The step of analyzing the second area information to obtain second remote sensing data includes: Acquire third optimized data based on the second area information, and determine the third optimized data as second remote sensing data; wherein the third optimized data is used to indicate low-resolution remote sensing data in the remote sensing reception information corresponding to the second area information.

2. The remote sensing data processing method according to claim 1, It is characterized in that The step of obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data includes: Performing spatial registration on the first remote sensing data and the second remote sensing data to obtain optimized remote sensing data; A remote sensing image is generated based on the optimized remote sensing data.

3. A remote sensing data processing system, It is characterized in that include: An acquisition module, used to acquire remote sensing reception information; wherein the remote sensing reception information is used to reflect the radiation data reflected from the surface to the receiver; A 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 area on the observed surface whose structural complexity is higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed surface whose structural complexity is lower than the preset complexity threshold; A second analysis module, used for analyzing the first area information to obtain first remote sensing data; A third analysis module, used for analyzing the second area information to obtain second remote sensing data; An integration module, configured to obtain a remote sensing image based on the first remote sensing data and the second remote sensing data; Wherein, the first analysis module is further used for: Analyzing the remote sensing received information to obtain structural feature information; wherein the structural feature information includes the elevation change of the structure of the observed surface and the boundary of the ground object; Analyze the structural feature information to obtain first region information and second region information; The analyzing according to the structural feature information to obtain the first region information and the second region information includes: Analyze the structural feature information to obtain first feature information and second feature information; wherein the first feature information is different from the second feature information; Obtain first region information based on the first feature information, and obtain second region information based on the second feature information; The step of analyzing the structural feature information to obtain the first feature information and the second feature information includes: Analyzing the feature boundary in the structural feature information to obtain a curvature feature; the curvature feature is used to reflect the degree of change of the feature boundary; Comparing the curvature feature 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; Analyze the elevation change, the first curvature information, and the second curvature information in the structural feature information to obtain first feature information and second feature information; 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: Analyzing the elevation change 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 the elevation change greater than or equal to a preset threshold, and the second complexity feature is used to reflect the structural feature information corresponding to the elevation change less than the preset threshold; Calculating a 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; The 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 the preset association value; the 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; The second analysis module is also used for: Acquire first optimization data and second optimization data based on the first area information; wherein the first optimization data is used to indicate high-resolution remote sensing data in the remote sensing reception information corresponding to the first area information, and the second optimization data is used to indicate low-resolution remote sensing data in the remote sensing reception information corresponding to the first area information; Analyze the first optimized data and the second optimized data to obtain first remote sensing data; The step of analyzing the first optimized data and the second optimized data to obtain first remote sensing data includes: Extracting features according to the second optimized data to obtain important feature information; wherein the important feature information is used to reflect the features that need to be processed in the first region information; Fusion of the important feature information with the first optimized data to obtain first remote sensing data; The third analysis module is also used for: Acquire third optimized data based on the second area information, and determine the third optimized data as second remote sensing data; wherein the third optimized data is used to indicate low-resolution remote sensing data in the remote sensing reception information corresponding to the second area information.

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