Remote sensing data processing method, system and equipment

Abstract

The invention is suitable for the technical field of remote sensing data processing, and particularly relates to a remote sensing data processing method and application thereof. Analyzing according to the remote sensing receiving information to obtain first area information and second area information; analyzing according to the first area information to obtain first remote sensing data; performing analysis according to the second region 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. According to the method, optimization processing can be performed on different areas in a targeted manner, the problem of one-step cutting in a traditional method is avoided, the information amount is guaranteed, consumption of computing resources is reduced, the processing efficiency is improved, the overall quality of the image is improved, and improvement of the consistency and integrity of information of different areas is facilitated.

Description

Technical Field

[0001] This application belongs to the technical field of remote sensing data processing, and particularly relates to a remote sensing data processing method and its application. Background Art

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

[0003] Traditional remote sensing data processing methods usually adopt a unified processing flow and process all data in the same way. This method has a slow processing speed and may cause information loss or poor processing effects in some areas, resulting in an unsatisfactory remote sensing image. Summary of the Invention

[0004] Embodiments of this application provide 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: Obtain 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; Analyze based on the remote sensing reception information to obtain first region information and second region information; wherein, the first region information is used to reflect the region on the observed earth's surface with a structural complexity higher than a preset complexity threshold, and the second region information is used to reflect the region on the observed earth's surface with a structural complexity lower than the preset complexity threshold; Analyze based on the first region information to obtain first remote sensing data; Analyze based on the second region information to obtain second remote sensing data; Obtain a remote sensing image based on the first remote sensing data and the second remote sensing data.

[0006] The above technical solutions in the embodiments of this application have at least the following technical effects: The remote sensing data processing method provided by this application obtains remote sensing reception information for reflecting the radiation data reflected from the ground surface to the receiver; then analyzes the remote sensing reception information to obtain first region information for reflecting regions in the observed ground surface with a structural complexity higher than a preset complexity threshold and second region information for reflecting regions in the observed ground surface with a structural complexity lower than the preset complexity threshold; further analyzes based on the first region information to obtain first remote sensing data; analyzes based on the second region 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. By analyzing the remote sensing reception information, identifying and distinguishing the first region information and the second region information, analyzing the first region information and the second region information separately to obtain different remote sensing data, and finally integrating the different remote sensing data of different regions to obtain a remote sensing image, this method can perform targeted optimization processing on different regions, avoiding the one-size-fits-all problem in traditional methods. While ensuring the amount of information, it reduces the consumption of computing resources, improves the 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 regions.

[0007] In a possible implementation manner of the first aspect, the analyzing based on the remote sensing reception information to obtain the first region information and the second region information includes: Analyzing based on the remote sensing reception information to obtain structural feature information; wherein, the structural feature information includes the elevation change of the structure of the observed ground surface and the object boundary. Analyzing based on the structural feature information to obtain the first region information and the second region information.

[0008] In a possible implementation manner of the first aspect, the analyzing based on the structural feature information to obtain the first region information and the second region information includes: Analyzing based on 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. Obtaining the first region information based on the first feature information and obtaining the second region information based on the second feature information.

[0009] In a possible implementation manner of the first aspect, the analyzing based on the structural feature information to obtain the first feature information and the second feature information includes: Analyzing based on the object boundary in the structural feature information to obtain a curvature feature; the curvature feature is used to reflect the degree of change of the object boundary. Compare 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 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 less than the preset curvature; Analyze based on 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.

[0010] In a possible implementation manner of the first aspect, the analyzing based on 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 includes: Analyze based on 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; Calculate the correlation value between the second curvature information and the first complexity feature; wherein, the correlation value is used to reflect the correlation degree between the second curvature information and the first complexity feature; Obtain first feature information based on the first curvature information, the first complexity feature, and the second curvature information corresponding to the correlation value greater than or equal to a preset correlation value; obtain second feature information based on the second complexity feature and the second curvature information corresponding to the correlation value less than the preset correlation value.

[0011] In a possible implementation manner of the first aspect, the analyzing based on the first region information to obtain first remote sensing data includes: Obtain first optimized data and second optimized data based on the first region information; wherein, the first optimized data is used to indicate the high-resolution remote sensing data in the remote sensing reception information corresponding to the first region information, and the second optimized data is used to indicate the low-resolution remote sensing data in the remote sensing reception information corresponding to the first region information; Analyze based on the first optimized data and the second optimized data to obtain first remote sensing data.

[0012] In a possible implementation manner of the first aspect, the analyzing based on the first optimized data and the second optimized data to obtain first remote sensing data includes: Extract 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 area information. Fuse the important feature information with the first optimized data to obtain the first remote sensing data.

[0013] In a possible implementation manner of the first aspect, the analyzing according to the second area information to obtain the second remote sensing data includes: Obtain the third optimized data based on the second area information, and determine the third optimized data 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.

[0014] In a possible implementation manner of the first aspect, the obtaining the remote sensing image based on the first remote sensing data and the second remote sensing data includes: Perform spatial registration on the first remote sensing data and the second remote sensing data to obtain the optimized remote sensing data. Generate a remote sensing image based on the optimized remote sensing data.

[0015] In a second aspect, an embodiment of the present application provides a remote sensing data processing system, including: An acquisition module, configured to acquire remote sensing reception information; wherein, the remote sensing reception information is used to reflect the radiation data reflected from the ground surface to the receiver. A first analysis module, configured to analyze according to the remote sensing reception information to obtain first area information and second area information; wherein, the first area information is used to reflect the area on the observed ground surface with a structural complexity higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed ground surface with a structural complexity lower than the preset complexity threshold. A second analysis module, configured to analyze according to the first area information to obtain the first remote sensing data. A third analysis module, configured to analyze according to the second area information to obtain the 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.

[0016] In a third aspect, an embodiment of the present application provides a remote sensing data processing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method described in any item of the first aspect above is implemented.

[0017] Fourthly, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method described in any one of the above first aspects is implemented.

[0018] Fifthly, an embodiment of the present application provides a computer program product, and when the computer program product runs on a remote sensing data processing device, the remote sensing data processing device is enabled to execute the remote sensing data processing method described in any one of the above first aspects.

[0019] It can be understood that the beneficial effects of the above second to fifth aspects can be referred to the relevant descriptions in the above first aspect, and will not be elaborated here. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0021] Figure 1 is a schematic flowchart of the remote sensing data processing method provided by the embodiment of the present application; Figure 2 is a schematic flowchart of the implementation of step S221 in the remote sensing data processing method provided by the embodiment of the present application; Figure 3 is a schematic structural diagram of the remote sensing data processing system provided by the embodiment of the present application; Figure 4 is a schematic structural diagram of the remote sensing data processing device provided by the embodiment of the present application. Detailed Embodiments

[0022] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can 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 avoid unnecessary details from interfering with the description of the present application.

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

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

[0025] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrases "if determined" or "if the described condition or event is detected" can be interpreted as meaning "once determined", "in response to determining", "once the described condition or event is detected", or "in response to detecting the described condition or event" depending on the context.

[0026] In addition, in the description of the specification and appended claims of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0027] Referring to "one embodiment" or "some embodiments" described in the specification of this application means that specific features, structures, or characteristics described in connection with that embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0028] Remote sensing technology is a technology that collects information on the earth's surface or atmosphere from a distance (such as platforms like satellites, airplanes, drones, etc.) through sensors. These sensors can measure the electromagnetic wave energy reflected, emitted, or scattered from the earth's surface and convert it into data for analysis. Remote sensing technology has been widely applied in multiple fields, such as geographic information systems (GIS), environmental monitoring, disaster management, agriculture, forestry, urban planning, etc.

[0029] 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 result in information loss or poor processing effects in some areas, thus resulting in an unsatisfactory remote sensing image.

[0030] To solve the above problems, an embodiment of the present application provides a remote sensing data processing method and its application. In this method, remote sensing reception information for reflecting the radiation data reflected from the ground surface to the receiver is obtained; then, based on the analysis of the remote sensing reception information, first region information for reflecting the region of the observed ground surface with a structural complexity higher than a preset complexity threshold and second region information for reflecting the region of the observed ground surface with a structural complexity lower than the preset complexity threshold are obtained; then, based on the analysis of the first region information, first remote sensing data is obtained; based on the analysis of the second region information, second remote sensing data is obtained; and a remote sensing image is obtained based on the first remote sensing data and the second remote sensing data. By analyzing the remote sensing reception information, identifying and distinguishing the first region information and the second region information, analyzing the first region information and the second region information separately to obtain different remote sensing data, and finally integrating the different remote sensing data of different regions to obtain a remote sensing image, this method can perform targeted optimization processing on different regions, avoiding the one-size-fits-all problem in traditional methods, reducing the consumption of computing resources while ensuring the amount of information, improving the processing efficiency, and not only improving the overall quality of the image, but also being conducive to improving the consistency and integrity of information in different regions.

[0031] The remote sensing data processing method provided by the embodiment of the present application can be applied to a remote sensing data processing device. At this time, the remote sensing data processing device is the execution subject of the remote sensing data processing method provided by the embodiment of the present application, and the embodiment of the present application does not impose any restrictions on the specific type of the remote sensing data processing device.

[0032] For example, the remote sensing data processing device can be a mobile phone, a tablet computer, a wearable device, an augmented reality (AR) / virtual reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a smart large screen, a smart TV and other terminal devices, a handheld device with wireless communication function, a computing device or other processing devices 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 set top box (STB), a customer premise 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).

[0033] To better understand the remote sensing data processing method provided in the embodiments of the present application, the following provides an exemplary introduction to the specific implementation process of the remote sensing data processing method provided in the embodiments of the present application.

[0034] Figure 1 The schematic flowchart of the remote sensing data processing method provided in the embodiments of the present application is shown. The remote sensing data processing method includes: S100, obtaining remote sensing reception information; where the remote sensing reception information is used to reflect the radiation data reflected from the ground surface to the receiver.

[0035] It can be understood that the remote sensing reception information can be collected by using sensors on satellites, airplanes or other platforms to collect remote sensing data. The sensors can be optical (such as multi-spectral, hyperspectral imagers), or non-optical (such as radar). It is also possible to receive remote sensing data simultaneously through sensors at multiple different spatial scales, that is, using a satellite platform to obtain macroscopic information on a large scale, using an airplane or drone platform to obtain high-resolution information on a local area, and so on, but not limited thereto.

[0036] S200, analyzing according to the remote sensing reception information to obtain first region information and second region information; where the first region information is used to reflect the region on the observed ground surface with a structural complexity higher than a preset complexity threshold, and the second region information is used to reflect the region on the observed ground surface with a structural complexity lower than the preset complexity threshold.

[0037] It can be understood that the preset complexity threshold is a preset complexity value, which can be manually input by humans, or obtained from a remote sensing database, and so on, but not limited thereto. The setting of the preset complexity threshold can be carried out by means such as laboratory experiments and past experience. The 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 by means such as laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining the data, the collected data is sorted, classified and archived, useful information and rules are extracted, and then the relevant data is saved to the database to form a remote sensing database.

[0038] The radiation data reflected by different types of observed objects are different, and for the same type of observed objects, their reflected radiation data are also different due to different geographical locations. After obtaining remote sensing received information, by comparing and analyzing each data, and through analogical classification based on the types of received data and the reception time on the spatial scale, all remote sensing data can be divided into first regional information and second regional information; or the remote sensing received information can be directly input into a machine learning model, and the machine learning model then outputs the first regional information and the second regional information, and so on, but not limited to this. The machine learning model is trained with a large amount of labeled remote sensing data, and the labeled remote sensing data labels the first regional information and the second regional information.

[0039] In a possible implementation, please refer to Figure 2 , in step S200, analyzing according to the remote sensing received information to obtain the first regional information and the second regional information, including: S210, analyzing according to 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 ground surface and the object boundary.

[0040] It can be understood that the elevation change refers to the height change of the ground surface, which can be obtained through digital elevation model (DEM) analysis. The elevation change reflects the undulation of the terrain. The object boundary refers to the boundary between different objects on the ground surface, such as the dividing line between roads, rivers, buildings, etc. The object boundary reflects the shape and distribution of the objects. The DEM data and multispectral data can be extracted from the remote sensing received information, and then the DEM data is analyzed, such as analyzing the time interval of the received data, calculating the time interval to obtain the corresponding elevation change, and then analyzing the multispectral data, such as analyzing the types of received data or radiation bands, to obtain the corresponding observed objects. By analyzing the continuous relationship between the observed objects, the object boundary can be obtained.

[0041] S220, analyzing according to the structural feature information to obtain the first regional information and the second regional information.

[0042] Exemplarily, the structural feature information can be identified, and divided according to the magnitude of the reflected elevation change and the complexity of the object boundary, that is, by respectively setting an elevation change threshold and an object boundary threshold, and then comparing and distinguishing the structural feature information with these two thresholds to obtain the first regional information and the second regional information; or the structural feature information can be input into a machine learning model, and the machine learning model then outputs the corresponding first regional information and the second regional information, and so on, but not limited to this.

[0043] 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.

[0044] In a possible implementation, in step S220, analyzing the structural feature information to obtain the first region information and the second region information includes: 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.

[0045] 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.

[0046] 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: 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.

[0047] 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).

[0048] S2212. Compare the curvature features with a preset curvature to obtain first curvature information and second curvature information. Among them, the first curvature information is used to reflect the structural feature information corresponding to the curvature features 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 features less than the preset curvature.

[0049] It can be understood that the preset curvature is a preset curvature value, which can be input manually by humans or obtained from a remote sensing database, etc., but not limited to this. After comparing each curvature value in the curvature features with the preset curvature, two groups of curvature value sets are obtained. One group has curvature values greater than or equal to the preset curvature, and the other group has curvature values 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.

[0050] S2213. Analyze based on the elevation change, first curvature information, and second curvature information in the structural feature information to obtain first feature information and second feature information.

[0051] Exemplarily, the first feature information and the second feature information can be obtained by classifying the elevation change, first curvature information, and second curvature information in the structural feature information respectively; or the elevation change, first curvature information, and second curvature information can be weighted and divided 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.

[0052] With such a setting, by comprehensively considering the elevation change and curvature information, the surface features can be described more accurately, avoiding the deviation that may be brought by single feature analysis, and at the same time providing quantitative data support for the first feature information and the second feature information. The first feature information reflects the detailed features of complex areas, and the second feature information reflects the basic features of simple areas. This comprehensive analysis helps to provide high-quality surface information to support further applications and decisions.

[0053] In a possible implementation, please refer to Figure 2 , in step S2213, analyze based on the elevation change, first curvature information, and second curvature information in the structural feature information to obtain first feature information and second feature information, including: S22131. Analyze based on the elevation change in the structural feature information to obtain a first complexity feature and a second complexity feature. Among them, 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.

[0054] It can be understood that the preset threshold is a preset value of the degree of elevation change, which can be manually input by humans or obtained from a remote sensing database, etc., but is not limited thereto.

[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 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 can be represented by the co-occurrence frequency between the second curvature information and the first complexity feature, etc., but is not limited thereto. 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 the high-complexity terrain, while a larger spatial distance indicates a weaker association. Or by statistically calculating correlation coefficients (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) to statistically calculate the co-occurrence frequency of the low-curvature area and the high-complexity terrain area. For example, by calculating the frequency of the simultaneous presence of low curvature and high-complexity terrain within a certain range to reflect the correlation between these two features.

[0057] S22133, obtain the first feature information based on the first curvature information, the first complexity feature, and the second curvature information corresponding to the correlation value being greater than or equal to the preset correlation value; obtain the second feature information based on the second complexity feature and the second curvature information corresponding to the correlation value being less than the 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 the correlation value being greater than or equal to the preset correlation value, and the second feature information includes the second complexity feature and the second curvature information corresponding to the correlation value being less than the preset correlation value.

[0059] With such a setting, by combining elevation change, curvature, and correlation value, the surface feature area can be more accurately identified. This method can distinguish high-complexity and low-complexity surface areas in detail, while considering elevation change and curvature features. This detailed classification helps to more accurately describe the complexity of surface features. The multi-dimensional analysis combining elevation change, curvature information, and correlation value brings efficient and accurate data processing and analysis results, which helps to improve feature recognition, enhance correlation analysis, and achieve more accurate and comprehensive surface feature analysis.

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

[0061] It can be understood that by calculating the proportion of the first feature information and the second feature information, the first feature information or the second feature information with a smaller proportion can be preferentially marked. That is, if the proportion of the first feature information is small, the first feature information is subjected to area division and marking, and the other second feature information is marked as the second area information.

[0062] With such a setting, the first feature information and the second feature information are obtained through structural feature information analysis, and the first feature information and the second feature information are converted into actual area information, that is, the first area and the second area are respectively divided based on the first feature information and the second feature information. This method improves the accuracy and efficiency of area division, supports in-depth terrain analysis, and provides a scientific basis and decision-making support for various practical applications.

[0063] S300. Analyze according to the first area information to obtain the first remote sensing data.

[0064] It can be understood that the first remote sensing data is used to generate a remote sensing image of the first area information. After the first area information is regionally marked, the remote sensing reception information corresponding to the first area information can be extracted from the remote sensing reception information. The remote sensing reception information corresponding to the first area information includes remote sensing data at different spatial scales, that is, it includes high-resolution remote sensing data and low-resolution remote sensing data. By analyzing the high-resolution remote sensing data and the low-resolution remote sensing data, the first remote sensing data can be obtained; the high-resolution remote sensing data corresponding to the first area information can also be directly determined as the first remote sensing data, and so on, but not limited to this.

[0065] In a possible implementation manner, in step S300, analyzing according to the first area information to obtain the first remote sensing data includes: S310. Obtain the first optimized data and the second optimized data based on the first area information; wherein, the first optimized data is used to indicate the high-resolution remote sensing data in the remote sensing reception information corresponding to the first area information, and the second optimized data is used to indicate the low-resolution remote sensing data in the remote sensing reception information corresponding to the first area information.

[0066] Exemplarily, after the first area information is determined, the first area information can be located and then matched with the remote sensing reception information to obtain the corresponding first optimized data and second optimized data; the high-resolution remote sensing data and the low-resolution remote sensing data in the remote sensing reception information can also be mapped into the first area information to obtain the corresponding first optimized data and second optimized data, and so on, but not limited to this.

[0067] S320. Analyze according to the first optimized data and the second optimized data to obtain the first remote sensing data.

[0068] Exemplarily, the first remote sensing data can be obtained by extracting features from the second optimized data, removing redundant parts in the second optimized data, and then fusing the first optimized data with the processed second optimized data; or by weighting the first optimized data and the second optimized data, multiplying them, and then normalizing the multiplied data to obtain the first remote sensing data, and so on, but not limited thereto.

[0069] In a possible implementation manner, in step S320, analyzing according to the first optimized data and the second optimized data to obtain the first remote sensing data includes: S321, extracting features from 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 area information.

[0070] Exemplarily, all important features can be obtained from the remote sensing database, and then matched with the second optimized data, and all the important features of the second optimized data that match the remote sensing database are marked and summarized to obtain the important feature information; or the second optimized data can be input into a learning model, and the learning model outputs the corresponding important feature information, and so on, but not limited thereto. 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, by collecting a large amount of low-resolution remote sensing data of the surface and the identifiers of the corresponding important features to train the learning model.

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

[0072] Exemplarily, the first remote sensing data can be obtained by spatially registering the first optimized data and the important feature information, then superimposing them, and performing normalization and smoothing processing on the superimposed data; or by fusing the feature descriptors of the important feature information and the first optimized data, and further processing the fused data, such as normalization, smoothing, etc., to obtain the first remote sensing data, and so on, but not limited thereto.

[0073] With such a setting, by fusing the first optimized data with the important feature information in the second optimized data, the first remote sensing data is obtained, which retains the high-resolution detail information and contains the key feature information. At the same time, the remote sensing data of the first area information can be quickly analyzed and obtained, accelerating the analysis process and improving the integrity and accuracy of the remote sensing data.

[0074] S400, analyzing according to the second area information to obtain the second remote sensing data.

[0075] It can be understood that the surface structure reflected by the second region information is relatively simple. After marking the second region 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 area size reflected by the second region information, etc., but not limited to this.

[0076] In a possible implementation manner, in step S400, analyzing according to the second region information to obtain the second remote sensing data includes: Obtaining third optimized data based on the second region information and determining the third optimized data 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 region information.

[0077] 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 received information as the second remote sensing data.

[0078] S500, obtaining a remote sensing image based on the first remote sensing data and the second remote sensing data.

[0079] It can be understood 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, they can be registered and fused through a spatial registration algorithm to obtain a complete remote sensing image; or all the feature points in the remote sensing received information can be respectively matched with the first remote sensing data and the second remote sensing data to map the first remote sensing data and the second remote sensing data into the same space at the same time, and then the mapped remote sensing data is transformed to generate a remote sensing image, etc., but not limited to this.

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

[0081] Exemplarily, the first remote sensing data and the second remote sensing data can be preprocessed, including denoising, smoothing, etc., and then feature points are 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 are matched. Through feature point matching, the corresponding points between the first remote sensing data and the second remote sensing data are found. According to the matched feature points, spatial registration is performed, and the registered first remote sensing data and second remote sensing data are merged to generate optimized remote sensing data.

[0082] S520, generating a remote sensing image based on the optimized remote sensing data.

[0083] Exemplarily, the optimized remote sensing data can be processed by using a pixel-level fusion method to fuse the high-resolution part and the low-resolution part in the optimized remote sensing data, and then calculate the geometric transformation parameters to convert them into corresponding remote sensing images.

[0084] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0085] Corresponding to the remote sensing data processing method described in the above embodiments, an embodiment of the present application further 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 The block diagram of the remote sensing data processing system provided by the embodiment of the present application is shown. For the convenience of description, only the parts related to the embodiment of the present application are shown.

[0086] Referring to Figure 3 , the remote sensing data processing system includes: An acquisition module, configured to acquire remote sensing reception information; wherein, the remote sensing reception information is used to reflect the radiation data reflected from the ground surface to the receiver.

[0087] A first analysis module, configured to analyze according to the remote sensing reception information to obtain first region information and second region information; wherein, the first region information is used to reflect the region on the observed ground surface with a structural complexity higher than a preset complexity threshold, and the second region information is used to reflect the region on the observed ground surface with a structural complexity lower than the preset complexity threshold.

[0088] A second analysis module, configured to analyze according to the first region information to obtain first remote sensing data.

[0089] A third analysis module, configured to analyze according to the second region information to obtain second remote sensing data.

[0090] An integration module, configured to obtain a remote sensing image based on the first remote sensing data and the second remote sensing data.

[0091] It should be noted that the information interaction, execution process, etc. between the above modules, due to being based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details are not described herein again.

[0092] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned modules is used as an example. In actual applications, the above functions can be allocated to different modules according to needs, that is, the internal structure of the system can be divided into different modules to complete all or part of the functions described above. Each module 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 a software functional unit. In addition, the specific names of the modules are only for the convenience of mutual distinction and do not limit the protection scope of this application. For the specific working process of the modules in the above system, reference can be made to the corresponding process in the foregoing method embodiment, which will not be elaborated here.

[0093] An embodiment of this application also provides a remote sensing data processing device. Figure 4 FIG. 5 is a schematic structural diagram of a remote sensing data processing device 6 provided in an embodiment of this application. As Figure 4 shown, the remote sensing data processing device 6 of this embodiment includes: at least one processor 60 ( Figure 4 only one is shown here), at least one memory 61 ( Figure 4 only one is shown here), 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 functions of each module in the above-mentioned system embodiments.

[0094] Exemplarily, the computer program 62 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to 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 computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The remote sensing data processing device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 4 merely examples of the remote sensing data processing device 6 are given and do 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. For example, it may also include input / output devices, network access devices, buses, etc.

[0096] The processor 60 may be a Central Processing Unit (CPU), or it may also 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 may be a microprocessor or any conventional processor, etc.

[0097] In some embodiments, the memory 61 may be an internal storage unit of the remote sensing data processing device 6, such as the 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, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc. equipped on the remote sensing data processing device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the remote sensing data processing device 6. The memory 61 is used to store an operating system, application programs, a 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.

[0098] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0099] An embodiment of the present application provides a computer program product, and when the computer program product runs on a remote sensing data processing device, the remote sensing data processing device is enabled to implement the steps in any of the above method embodiments.

[0100] When 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, to implement all or part of the processes in the above-described embodiment methods of this application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the remote sensing data processing device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc.

[0101] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0102] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0103] In the embodiments provided in this application, it should be understood that the disclosed remote sensing data processing device and system can be implemented in other ways. For example, the above-described remote sensing data processing system embodiments are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or modules can be in an electrical, mechanical, or other form.

[0104] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module, that is, it may be located in one place or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] The above embodiments 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements 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 the present application, and should all be included in the protection scope of the present application.

Claims

1. A remote sensing data processing method, comprising: Obtaining remote sensing received information, where the remote sensing received information is used to reflect the radiation data reflected from the ground surface to the receiver; Analyzing according to the remote sensing received information to obtain first area information and second area information, where the first area information is used to reflect the area on the observed ground surface with a structural complexity higher than a preset complexity threshold, and the second area information is used to reflect the area on the observed ground surface with a structural complexity lower than the preset complexity threshold; Analyzing according to the first area information to obtain first remote sensing data; Analyzing according to 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.

2. The method according to claim 1, wherein The analyzing according to the remote sensing received information to obtain first area information and second area information includes: Analyzing according to the remote sensing received information to obtain structural feature information, where the structural feature information includes the elevation change and the object boundary of the structure of the observed ground surface; Analyzing according to the structural feature information to obtain first area information and second area information may include: Analyzing according to the structural feature information to obtain first feature information and second feature information, where the first feature information is different from the second feature information; Obtaining first area information based on the first feature information and obtaining second area information based on the second feature information; The analysis operation of obtaining the first feature information and the second feature information may include: Analyzing according to the object boundary in the structural feature information to obtain a curvature feature; the curvature feature is used to reflect the change degree of the object boundary; Comparing the curvature feature with a preset curvature to obtain first curvature information and second curvature information, where the first curvature information is used to reflect the structural feature information corresponding to the curvature feature 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 less than the preset curvature; Analyzing according to 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.

3. The method according to the preceding claim, characterized in that, The analyzing according to 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 includes: Analyzing according to the elevation change in the structural feature information to obtain first complexity feature and second complexity feature, where 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 the correlation value between the second curvature information and the first complexity feature, where the correlation value is used to reflect the correlation degree 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 a 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.

4. The method according to claim 1, characterized in that, The analysis based on the first region information to obtain the first remote sensing data includes: The first optimized data and the second optimized data are obtained based on the first region information, where the first optimized data is used to indicate the high-resolution remote sensing data in the remote sensing reception information corresponding to the first region information, and the second optimized data is used to indicate the low-resolution remote sensing data in the remote sensing reception information corresponding to the first region information; The first remote sensing data is obtained through analysis based on the first optimized data and the second optimized data.

5. The method according to the preceding claim, characterized in that, The analysis based on the first optimized data and the second optimized data to obtain the first remote sensing data includes: Feature extraction is performed on the second optimized data to obtain important feature information, where 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.

6. The method according to claim 1, wherein The analysis based on the second region information to obtain the second remote sensing data includes: The third optimized data is obtained based on the second region information, and the third optimized data is determined as the second remote sensing data, where the third optimized data is used to indicate the low-resolution remote sensing data in the remote sensing reception information corresponding to the second region information.

7. The method according to claim 1, characterized in that, The obtaining of the remote sensing image based on the first remote sensing data and the second remote sensing data includes: The first remote sensing data and the second remote sensing data are spatially registered to obtain optimized remote sensing data; A remote sensing image is generated based on the optimized remote sensing data.

8. A remote sensing data processing system, comprising: An acquisition module, configured to acquire remote sensing reception information; wherein, the remote sensing reception information is used to reflect the radiation data reflected from the ground surface to the receiver; A first analysis module, configured to perform analysis based on the remote sensing reception information to obtain first region information and second region information; wherein, the first region information is used to reflect the region on the observed ground surface with a structural complexity higher than a preset complexity threshold, and the second region information is used to reflect the region on the observed ground surface with a structural complexity lower than the preset complexity threshold; A second analysis module, configured to perform analysis based on the first region information to obtain the first remote sensing data; A third analysis module, configured to perform analysis based on the second region information to obtain the 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.

9. A remote sensing data processing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method according to any one of the above when executing the computer program.

10. A computer-readable storage medium storing a computer program, which when executed by a processor, implements the method described in any one of the above.

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