Multi-source remote sensing data processing method and device, storage medium and computer equipment

Abstract

The invention relates to the technical field of remote sensing detection, and provides a multi-source remote sensing data processing method and device, a storage medium and computer equipment, and the method comprises the steps: analyzing the uploaded data of different remote sensing equipment to obtain remote sensing data; uniformly accessing all remote sensing data to a data management system according to a data access rule, the data access rule defining at least one of a data format, a data coding rule and a data naming rule; calling required remote sensing data from the data management system according to the data demand, and preprocessing the remote sensing data; and performing fusion processing on the preprocessed remote sensing data to generate a fusion data set. According to the scheme, it is ensured that all remote sensing data are accessed according to a unified standard, the problem that the data are incompatible due to source diversity is solved, the standardization degree of the data is improved, the preprocessed remote sensing data are fused, data of different sources are integrated and unified, and sharing between different platforms and systems is facilitated.

Description

Technical Field

[0001] The present application relates to the field of remote sensing detection technology, and in particular to a multi-source remote sensing data processing method, device, storage medium and computer equipment. Background Art

[0002] Remote sensing, a non-contact, long-distance detection technology, is a crucial means of acquiring space information today. Satellite remote sensing platforms include various types, such as onboard lasers, cameras, and microwave imagers. Each platform has its own advantages, such as the ability of onboard lasers to achieve high-precision measurements and the immunity of microwave imagers to cloud and fog.

[0003] Due to differences in observation technology, remote sensing data acquired by different types of equipment exhibit significant discrepancies in acquisition time, image quality, and positioning accuracy. For example, while satellites can provide stereoscopic pixels from multiple images taken at different times over the same area, the resolution and positional accuracy of satellite imagery are limited. Ground-based cameras, while highly accurate, also require longer capture times. The remote sensing data used in current research comes from different devices, and the standards for generating these data are not uniform. This leads to compatibility issues between different remote sensing data, preventing direct comparison.

[0004] Therefore, how to provide a multi-source data processing method that can unify different data is a technical problem that those skilled in the art urgently need to solve. Summary of the Invention

[0005] The purpose of this application is to provide a multi-source remote sensing data processing method, device, storage medium and computer equipment, so that remote sensing data from different sources can be accessed with a unified standard, solving the technical problem of inconsistent and incompatible formats due to the diversity of data sources.

[0006] The technical solution of this application is as follows:

[0007] In a first aspect, a multi-source remote sensing data processing method is provided, comprising:

[0008] Parse the uploaded data of different remote sensing devices to obtain remote sensing data;

[0009] All the remote sensing data are uniformly accessed into a data management system according to data access rules, wherein the data access rules define at least one of a data format, a data encoding rule, and a data naming rule;

[0010] Retrieving required remote sensing data from the data management system according to data requirements and preprocessing the remote sensing data;

[0011] The pre-processed remote sensing data is fused to generate a fused data set.

[0012] Preferably, parsing uploaded data from different remote sensing devices to obtain remote sensing data includes:

[0013] receiving and parsing data uploaded by a synthetic aperture radar device to obtain synthetic aperture radar images therefrom; and / or,

[0014] Receive and parse data uploaded by a LiDAR device to obtain LiDAR images; and / or,

[0015] Receive and parse the data uploaded by the camera device to obtain camera images.

[0016] Preferably, when the required remote sensing data is a synthetic aperture radar image, preprocessing the synthetic aperture radar image includes:

[0017] Performing radiometric correction on the synthetic aperture radar image, including:

[0018] Correction of synthetic aperture radar image calibration coefficients;

[0019] Converting the complex format data in the synthetic aperture radar image into amplitude data based on the corrected synthetic aperture radar image calibration coefficient; and / or,

[0020] Performing multi-view processing on the synthetic aperture radar image includes:

[0021] Dividing the effective synthetic aperture length of the synthetic aperture radar image into multiple segments, each segment imaging the same scene to obtain corresponding image data;

[0022] summing and superimposing all imaged image data to generate a multi-view synthetic aperture radar image; and / or,

[0023] Performing geometric processing on the synthetic aperture radar image, including:

[0024] Performing slant-to-ground range conversion and geocoding on the synthetic aperture radar image, wherein the geocoding includes coding ellipsoid correction and geocoding terrain correction; and / or,

[0025] Perform filtering processing on the synthetic aperture radar image.

[0026] Preferably, when the required remote sensing data is a lidar image, preprocessing the lidar image includes:

[0027] Performing multi-source image matching on the laser radar image includes:

[0028] Identify key points between at least two lidar images for image matching.

[0029] Preferably, preprocessing the laser radar image further includes:

[0030] Performing block adjustment on the lidar image after multi-source image matching; and / or,

[0031] Orthorectification is performed on the lidar image obtained through multi-source image matching, including:

[0032] Converting oblique oblique images from LiDAR images to orthophotos; and / or,

[0033] Performing color grading on the laser radar image after multi-source image matching, wherein the color grading includes at least one of template color grading, geographic template color grading, and regional network color grading; and / or,

[0034] Performing image mosaicking on the laser radar image after multi-source image matching, including:

[0035] Performing radiation correction, geometric correction, registration and overlapping area processing on each of the laser radar images;

[0036] All of the laser radar images are combined and spliced ​​to form a mosaic image without overlapping areas.

[0037] Preferably, when the required remote sensing data is a camera image, preprocessing the camera image includes:

[0038] Performing image correction and format conversion on the camera image, including:

[0039] Rotating the camera image according to the yaw angle and tilt angle of the camera image; and / or,

[0040] Performing principal point correction processing on the camera image according to the center point position of the camera image; and / or,

[0041] performing distortion correction processing on the camera image according to the radial distortion parameter and the tangential distortion parameter of the camera image;

[0042] Perform format conversion processing on the original data of the camera image.

[0043] Preferably, preprocessing the camera image further includes:

[0044] Performing triangulation processing on the camera image after image correction and format conversion, including:

[0045] Extracting feature points from the camera image and performing matching processing on the feature points;

[0046] Performing relative and absolute orientation on the camera image using the matching results of the feature points;

[0047] performing adjustment on the oriented camera image; and / or,

[0048] Performing elevation processing on the camera image that has undergone image correction and format conversion, including:

[0049] extracting raw digital surface model data including height information of the ground and objects above the ground from the camera image;

[0050] Filtering the digital surface model data to separate the height information of the ground and objects above the ground, removing non-ground elements, and generating digital elevation model data; and / or,

[0051] The camera image that has undergone image correction and format conversion is subjected to orthorectification processing, and / or radiometric correction processing, and / or stitching processing.

[0052] Preferably, the step of uniformly accessing all the remote sensing data to the data management system according to the data access rules further comprises:

[0053] The remote sensing data that has been uniformly accessed is stored in a data management system according to data management rules, wherein the data management rules define the types of databases in the data management system and the data storage methods of different types of databases, the structure of the data and the storage methods of data with different structures.

[0054] Preferably, storing the remote sensing data accessed in a unified manner in a data management system according to data management rules includes:

[0055] Identifying a data structure of the remote sensing data, wherein the data structure includes structured data and semi-structured data;

[0056] Connect the file system to the database with multiple distributed storage nodes;

[0057] Dividing the structured remote sensing data into blocks to generate data blocks, and storing the data blocks in databases corresponding to different storage nodes;

[0058] The semi-structured remote sensing data is stored in the file system.

[0059] Preferably, before preprocessing the remote sensing data, the method further comprises:

[0060] Performing coordinate transformation on all the remote sensing data to unify the coordinate system of the remote sensing data;

[0061] Scale conversion is performed on all the remote sensing data to unify the scales of the remote sensing data, wherein the scale includes resolution and / or scale.

[0062] In a second aspect, a multi-source remote sensing data processing device is provided, comprising:

[0063] Parsing module, used to parse the uploaded data of different remote sensing devices to obtain remote sensing data;

[0064] An access module, configured to uniformly access all of the remote sensing data into a data management system according to data access rules, wherein the data access rules define at least one of a data format, a data encoding rule, and a data naming rule;

[0065] A processing module, configured to retrieve required remote sensing data from the data management system according to data requirements and pre-process the remote sensing data;

[0066] The fusion module is used to perform fusion processing on the pre-processed remote sensing data to generate a fused data set.

[0067] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0068] In a fourth aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the first aspect when executing the computer program.

[0069] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0070] The present application provides a multi-source remote sensing data processing method, apparatus, storage medium, and computer equipment. The method includes: parsing uploaded data from different remote sensing devices to obtain remote sensing data; uniformly accessing all remote sensing data to a data management system according to data access rules, wherein the data access rules define at least one of a data format, a data encoding rule, and a data naming rule; retrieving the required remote sensing data from the data management system and preprocessing the remote sensing data according to data requirements; and fusing the preprocessed remote sensing data to generate a fused data set. In the scheme, data access rules are formulated to ensure that all remote sensing data is accessed according to a unified standard, solving the problem of inconsistent formats and incompatibility caused by the diversity of data sources, improving the degree of data standardization, preprocessing remote sensing data, improving the quality of remote sensing data, and providing a detailed data foundation for subsequent fusion processing. Fusing remote sensing data integrates data from different sources into a unified data set, further enhancing data consistency and facilitating sharing and application across different platforms and systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. 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 creative work.

[0072] Figure 1 This is a general flow chart of the multi-source remote sensing data processing method provided in the embodiment of the present application;

[0073] Figure 2 This is a flowchart of obtaining remote sensing data provided by an embodiment of the present application;

[0074] Figure 3 is a schematic diagram of image matching provided in an embodiment of the present application;

[0075] Figure 4 This is a flowchart of the regional block adjustment provided by the embodiment of the present application;

[0076] Figure 5 This is a flowchart of the orthorectification provided by an embodiment of the present application;

[0077] Figure 6 2 is a schematic diagram showing the comparison before and after the orthorectification provided in an embodiment of the present application;

[0078] Figure 7 This is a schematic diagram of the template color uniformity provided in the embodiment of the present application;

[0079] Figure 8 This is a schematic diagram of a uniformly colored geographic template provided in an embodiment of the present application;

[0080] Figure 9 1 is a schematic diagram showing a comparison before and after the color uniformity of the regional network provided in an embodiment of the present application;

[0081] Figure 10 This is a relationship table diagram of the new map sheet number and each sub-frame number provided in the embodiment of the present application;

[0082] Figure 11 This is a flowchart of feature point extraction provided by an embodiment of the present application;

[0083] Figure 12 This is a flowchart of storing and managing multi-source remote sensing data provided by an embodiment of the present application;

[0084] Figure 13 This is a flowchart of searching and accessing data provided by an embodiment of the present application;

[0085] Figure 14 is a schematic diagram of a multi-source remote sensing data processing device provided in an embodiment of the present application;

[0086] Figure 15 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0087] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0088] As described in the background technology, the remote sensing data used in current research all come from different remote sensing equipment, and the standards for forming remote sensing data are not unified, resulting in compatibility issues between different remote sensing data and inability to directly compare and use them.

[0089] Based on this, the present application provides a multi-source remote sensing data processing method, which aims to solve the technical problems existing in the prior art of inconsistent and incompatible formats of data due to the diversity of sources.

[0090] Example 1

[0091] The first embodiment of the present application provides a multi-source remote sensing data processing method, referring to Figure 1 The overall flow chart of the method includes:

[0092] S10: parsing uploaded data from different remote sensing devices to obtain remote sensing data;

[0093] S20: All remote sensing data are uniformly accessed into the data management system according to the data access rules, wherein the data access rules define at least one of a data format, a data encoding rule, and a data naming rule;

[0094] S30: Retrieving the required remote sensing data from the data management system according to data requirements and preprocessing the remote sensing data;

[0095] S40: performing fusion processing on the pre-processed remote sensing data to generate a fused data set.

[0096] Among them, multi-source remote sensing data refers to remote sensing data with different sources. These remote sensing data come from different remote sensing equipment (such as satellites, sensor equipment mounted on satellites, aircraft and ground station platforms, etc.), time nodes and spatial regions. Different types of remote sensing data provide complementary information; remote sensing data contains actual observation information, usually including image information, such as spectral images such as visible light, infrared and multispectral, picture images such as SAR (synthetic aperture radar), and dynamic images such as video. The attributes of each image information record the location information of the image, such as GPS coordinates and geocoding, etc., time information such as the specific time point or time period of image acquisition, the changes of the image at different time points, and spatial information such as the specific location and longitude and latitude of image acquisition.

[0097] In step S10, multi-source remote sensing data are parsed to obtain each remote sensing data, which provides a basis for subsequent data access, preprocessing and fusion.

[0098] In step S20, due to the diverse sources of remote sensing data, including different remote sensing devices (such as satellites, sensor devices mounted on satellites, aircraft and ground station platforms, etc.), time nodes, and spatial regions, there are significant differences in data formats, resolutions, projection methods, and time bases between different data, resulting in the inability to directly access and use the data. Therefore, data access rules are introduced. Under these rules, all remote sensing data from different sources are uniformly accessed, ensuring that data from different sources can be accessed according to unified standards, solving the problems of diverse data sources and inconsistent formats, and facilitating subsequent data management. The data management system is used to store remote sensing data and can provide remote sensing data management, query, and directory services to staff.

[0099] In the definition of data access rules, data format refers to the storage format of remote sensing data, and encoding rules refer to the encoding methods used during storage and transmission. For example, remote sensing data includes image information, image formats include GeoTIFF and JPEG, vector formats include GeoJSON, and encoding rules include compression encoding, binary encoding, and spectral encoding. Data naming rules refer to the naming method of remote sensing data. For example, device-based naming such as "Satellite1_," "Moons_2," and "Sensor_3" represent Satellite 1, Satellite 2, and Sensor 3; time-based naming such as "20000101" represents data from January 1, 2000; spatial naming such as "Ground4_" represents Ground 4; device-based naming such as "_SAR" and "_RGB" represents radar image data and visible light data; and the combined naming such as "Sensor3_20000101_Ground4_SAR" represents radar image data captured by Sensor 3 on January 1, 2000, in Ground 4.

[0100] In an optional embodiment, the data access rules also define data standards, which limit the data content and quality to ensure the consistency and accuracy of the data, such as what information should be included in the remote sensing data, the accuracy of the information and the physical characteristics of the data; for example, it is stipulated that the spatial resolution of the remote sensing data is within a preset range, the band range of the remote sensing data is within a preset range, the positioning accuracy of the remote sensing data is within a preset value, and the remote sensing data contains complete information, that is, there is no data missing and other problems.

[0101] In step S30, the data required for the task is the data collected, such as data collected by synthetic aperture radar (SAR), lidar, or drones. Preprocessing, including but not limited to geometric correction, radiometric correction, atmospheric correction, and noise reduction, aims to improve the quality and consistency of remote sensing data and provide a detailed data foundation for subsequent fusion processing.

[0102] In step S40, by fusing remote sensing data from different sources, different times, and different spaces, the advantages of each data can be integrated to provide more comprehensive information.

[0103] In an optional embodiment, the method after unified access to remote sensing data further includes: classifying the remote sensing data according to data classification rules, wherein the data classification rules are set based on the data source, and / or time reference, and / or space reference. Since most remote sensing data are multi-source heterogeneous remote sensing data collected by aerospace equipment; these data include a large amount of spatial vector data and non-spatial vector remote sensing data. Due to the large amount of data and complex structure, the system is prone to confusion when processing these data after unified access due to the heterogeneity and complexity of the data, resulting in reduced processing efficiency. Therefore, data classification rules are introduced, under which the unified access remote sensing data are first classified to make the data easier to manage and integrate, so as to facilitate subsequent pre-processing and data fusion processing.

[0104] In the definition of data classification rules, the data source refers to the device that generates remote sensing data; for example, radar image data collected by radar equipment on satellite platforms, ground image data collected by camera equipment at ground observation stations, drone image data collected by aerial devices such as drones, and lidar image data collected by lidar equipment. The time reference refers to the time when the remote sensing data was collected, which can be a specific time point or time period; for example, the collection time point is "January 1, 2000" and the collection time period is "January 1, 2000 to January 5, 2000." The spatial reference refers to the geographic location of the remote sensing data, which can be a specific geographic coordinate or spatial region; for example, the longitude and latitude "(X°N, Y°E)" and the spatial region "desert" or "ocean."

[0105] For example, remote sensing data can be categorized based on the device used to collect it. For example, if the remote sensing data consists of images collected by radar and optical equipment, the high-resolution optical image data collected by the optical equipment can be fused with the low-resolution radar image data collected by the radar equipment to simultaneously obtain detailed information about ground features and the ability to penetrate clouds and fog. Furthermore, the fusion process can identify and eliminate duplicate data, avoiding the storage and processing of redundant data, thereby saving storage space and processing time.

[0106] When performing data fusion, the remote sensing data in each classification group can be fused according to different classification groups, and then the fused data of all classification groups can be fused again to generate a fused data set. In this way, since the remote sensing data in the same classification group have similar characteristics, such as the same source, the same time, or the same space, fusing these data can more accurately integrate information and reduce fusion errors caused by large data differences. In the final integration and fusion, the error accumulation caused by fusing a large amount of remote sensing data at one time is reduced, and the quality of the fusion result is improved.

[0107] In a specific embodiment, a multi-source remote sensing data processing system first acquires and parses uploaded data from different remote sensing devices to obtain remote sensing data and obtain core image information, etc.; then, the remote sensing data is uniformly and standardizedly accessed according to data access rules. For example, for image information, image processing software or a library can be used to convert the data into a specified image format. For vector data, it can be converted into a standard vector format such as GeoJSON. The converted data is named to ensure that the data name can reflect the source, time point, spatial area and type of the data for subsequent classification processing; then, the remote sensing data is classified according to the data classification rules, such as dividing the data uploaded by the sensor device into one group, the data uploaded by the satellite platform into one group, the data uploaded for time period A into one group, and the data captured in area B into one group. The remote sensing data in different groups are preprocessed and fused in turn, and then the data after fusion processing of all classified groups are fused again to generate a fused data set.

[0108] To sum up, formulating data access rules to ensure that all remote sensing data are accessed according to unified standards solves the problem of inconsistent formats and incompatibility of data due to diversity of sources, improves the standardization of data, preprocesses remote sensing data, improves the quality of remote sensing data, and provides a fine data foundation for subsequent fusion processing. Fusion processing of remote sensing data integrates data from different sources into a unified data set, further enhancing data consistency and facilitating sharing and application between different platforms and systems.

[0109] Preferably, reference Figure 2 Schematic diagram of obtaining remote sensing data, S10: parsing uploaded data from different remote sensing devices to obtain remote sensing data, including:

[0110] Receive and parse the data uploaded by the synthetic aperture radar device to obtain synthetic aperture radar images.

[0111] In one specific embodiment, the data acquisition device includes a synthetic aperture radar (SAR) device, and the multi-source remote sensing data processing system acquires data uploaded by the SAR device. When acquiring external data, the SAR device uses a small antenna as a single radiating unit, which is continuously moved along a preset path to receive echo signals from the same ground feature at different locations and perform correlation demodulation and compression processing to obtain images of the Earth's surface. When acquiring data uploaded by the SAR device, the system typically receives the compressed SAR data through a specialized SAR data receiving device, such as a ground station or satellite data receiver. After receiving the data, the system performs decompression and other reverse processing to obtain detailed and usable SAR image data.

[0112] Receive and parse the data uploaded by the lidar device to obtain lidar images.

[0113] In one specific embodiment, the data acquisition device includes a lidar device, and the multi-source remote sensing data processing system acquires data uploaded by the lidar device. When acquiring external data, the lidar device emits a laser beam and receives the reflected signal to measure the distance and shape of objects. The system receives and processes the data uploaded by the lidar device using a dedicated lidar data receiver or satellite data receiver. After receiving the data, it performs three-dimensional processing such as decoding and point cloud generation to obtain lidar images containing three-dimensional terrain or object models.

[0114] Receive and parse the data uploaded by the camera device to obtain camera images.

[0115] In one specific embodiment, the data acquisition device includes a camera device, and the multi-source remote sensing data processing system acquires data uploaded by the camera device. The system receives the scene image uploaded by the camera device via wireless transmission, performs decoding and image analysis, and other processing to obtain a camera image containing rich scene information.

[0116] It should be noted that the order of the process for acquiring remote sensing data from different devices is not limited. The following also exemplifies the working process of other types of data acquisition devices and the method of acquiring remote sensing data.

[0117] In one optional embodiment, the data acquisition device includes an infrared device, and the multi-source remote sensing data processing system acquires data uploaded by the infrared device. Infrared devices are typically installed on remote sensing satellites, unmanned aerial vehicles, or ground platforms. When collecting external data, they utilize sensors within infrared wavelength ranges, specifically near-infrared, mid-infrared, and far-infrared. When acquiring data uploaded by the infrared device, the system receives the infrared data via wireless transmission and performs decoding, calibration, and other processing to obtain accurate infrared images or temperature information.

[0118] In an optional embodiment, the data acquisition device includes a spectral device, and the multi-source remote sensing data processing system acquires data uploaded by the spectral device. The spectral device has multispectral / hyperspectral recognition capabilities. When acquiring external data, it simultaneously collects spectral information of ground objects through multiple spectral channels to obtain rich ground object characteristics. The system receives the uploaded data from the spectral device via wireless transmission and performs spectral processing such as decoding and spectral correction to obtain accurate multispectral / hyperspectral imagery.

[0119] In an optional embodiment, the UAV device or satellite device is equipped with at least one of a synthetic aperture radar device, a lidar device, a camera device, an infrared device and a spectral device.

[0120] Preferably, when the required remote sensing data is a synthetic aperture radar image, preprocessing the synthetic aperture radar image includes:

[0121] Perform radiometric correction on synthetic aperture radar images, including:

[0122] Correction of synthetic aperture radar image calibration coefficients;

[0123] The complex format data in the radar image is converted into amplitude data based on the corrected synthetic aperture radar image calibration coefficient.

[0124] The amplitude values ​​of SAR images reflect the backscattering characteristics of ground targets. To accurately assess these characteristics, SAR images require radiometric correction to obtain more precise amplitude or intensity values. Radiometric correction for SAR images primarily involves calibration coefficient correction and complex number conversion. First, SAR image calibration coefficient correction involves revising SAR image calibration parameters using corner reflectors deployed at a ground calibration field. This can be achieved through both absolute and relative calibration. Second, complex number data conversion involves converting SAR image complex data into intensity / amplitude data, as intensity / amplitude characteristics are one of the most important characteristics of SAR images. SAR intensity / amplitude images can be used to extract ground object information. This requires converting SAR image complex data into intensity / amplitude data. Complex data conversion converts complex number data into intensity, amplitude, phase, real part, and imaginary part.

[0125] Perform multi-look processing on synthetic aperture radar images, including:

[0126] The effective synthetic aperture length of the synthetic aperture radar image is divided into multiple segments, and each segment images the same scene to obtain corresponding image data;

[0127] All image data are summed and superimposed to generate a multi-view synthetic aperture radar image.

[0128] The effective synthetic aperture length of a SAR image is divided into multiple segments, each capturing the same scene. The resulting images are summed and superimposed to create a single, accurate SAR image. Multi-look processing averages the SAR image in azimuth and / or range, resulting in multi-look intensity data. SAR images processed through multi-look processing have reduced spatial resolution but improved radiometric resolution, meaning increased intensity.

[0129] Perform geometric processing on synthetic aperture radar images, including:

[0130] Perform slant-to-ground range conversion and geocoding on synthetic aperture radar images, where geocoding includes coding ellipsoid correction and geocoding terrain correction.

[0131] The need for slant-to-ground distance conversion stems from the fact that the distance measured by the synthetic aperture radar (SAR) device is the distance from the target to one side of the platform, i.e., the slant distance, which leads to distortion in real-world ground mapping. Assuming the terrain is flat, slant-to-ground distance correction can be used to resample the SAR image of the slant distance into an image with the same pixel size as the ground distance image. Based on the attitude and orbit data transmitted by the satellite device, the L1-level image data is geometrically positioned, projected, and resampled to form L2-level geocoding data. The geocoding function uses a geometric correction processing method based on the RD positioning model, including geocoding ellipsoid correction (GEC) and geocoding terrain correction (GTC).

[0132] Perform filtering on synthetic aperture radar images.

[0133] Among them, in order to reduce the speckle noise of the synthetic aperture radar image, it is necessary to perform filtering operations on the synthetic aperture radar image. The filtering modules include but are not limited to Frost filtering, EnFrost filtering, Lee filtering, EnLee filtering, Kuan filtering and Gamma filtering.

[0134] Preferably, when the required remote sensing data is a lidar image, preprocessing the lidar image includes:

[0135] Perform multi-source image matching on lidar images, including:

[0136] Identify key points between at least two lidar images for image matching.

[0137] Among them, the multi-source image matching algorithm is the process of identifying points of the same name between two or more images of the same or different types. It is a preliminary step in subsequent regional block adjustment, image registration, image fusion, target recognition and target change detection. Multi-source image matching involves images acquired from different sensors. These images may have differences in spatial resolution, imaging mode and spectral information. It can support matching between optical images, as well as matching between heterogeneous images such as optical, radar and vector images. Figure 3 Schematic diagram of image matching. The left image is the reference lidar image, the middle image is the lidar image to be registered, and the right image is the lidar image after registration. Identify and match the same points in the reference image and the image to be registered.

[0138] Preferably, preprocessing the lidar image further includes:

[0139] Perform block adjustment on lidar images that have undergone multi-source image matching.

[0140] Block adjustment, an adjustment method used for aerial triangulation, is widely used in surveying, photogrammetry, and remote sensing. Its main purpose is to improve the geometric accuracy and consistency of images by processing regions composed of multiple flight routes through global adjustment. The basic principle of block adjustment is to use control points and tie points to optimize the internal and external orientation elements of an image, thereby improving the relative geometric accuracy and absolute positioning accuracy between images. Joint block adjustment technology is an important method for improving the positioning accuracy of multi-source heterogeneous remote sensing data. Due to the inconsistency between the modeling models and error equations of different sensor data, joint adjustment cannot be performed directly using the adjustment method of a single data source. Joint adjustment of multi-source data involves different observation perspectives such as satellite, aerial, low-altitude, and ground observation, as well as data from different observation modalities such as optical, microwave, and laser. It requires the establishment of error models for various types of images and the resolution of the correlation and variance component estimation issues between different original observation data.

[0141] refer to Figure 4 This block adjustment flow chart aims to optimize and correct the geometric accuracy of remote sensing imagery. Inputs include RFM RRCs, control points, tie points, and weak intersection points for block adjustment. During the adjustment process, convergence is determined. If not, adjustments are made using DEM interpolated elevations. If convergence is achieved, the adjustment concludes. The 3D coordinates of the weak intersection points are fed back during the adjustment process for further adjustments.

[0142] Orthorectify the lidar image after multi-source image matching, including:

[0143] Convert oblique oblique images from lidar images to orthophotos.

[0144] Among them, orthorectification is a technology used to correct geometric distortion caused by terrain undulations, sensor attitude and system errors in satellite or aerial images. Its purpose is to convert tilted oblique images into orthophotos so that the image pixels remain parallel to the surface features, thereby providing a view equivalent to a vertical aerial photograph.

[0145] refer to Figure 5 Orthorectification flow chart and Figure 6 The original image and orbit parameters are used as input, and the RPC parameters are calculated through the geometric model. Then, the DEM control points and the original image are combined to perform orthorectification using the RPC model, and finally an orthophoto is generated. Figure 6 In the figure, the left image is the image before orthophoto processing, and the right image is the image after orthophoto processing.

[0146] Color uniformity processing is performed on the lidar image after multi-source image matching, wherein the color uniformity processing includes at least one of template color uniformity, geographic template color uniformity and regional network color uniformity.

[0147] Image color grading is a key image processing technique that aims to adjust the color balance and brightness of an image to make the colors more uniform and natural. By grading the color, visual differences caused by lighting conditions, atmospheric conditions, or sensor variations can be eliminated, thereby enhancing image comparability and readability.

[0148] Specifically, refer to Figure 7 Schematic diagram of template color matching. Template color matching involves selecting an image with good scenery as a template and changing the overall color and hue of the image to be processed so that the processed image remains consistent with the color of the template. This method is suitable for sampling orthophotos and stitching them into images with a small amount of data, or directly adjusting the color of orthophotos to historical images.

[0149] Specifically, refer to Figure 8 Schematic diagram of geographic template color matching. Geographic template color matching uses the color mapping principle and requires that the geographic coverage of the template and the image to be processed have a certain overlapping area. Only the overlapping area is color matched. This method is suitable for aerial and satellite remote sensing images, especially in scenes that require color consistency processing in engineering applications, specifically lidar images.

[0150] Specifically, refer to Figure 9 A schematic diagram of the block color grading process. The left image shows the image before block color grading, and the right image shows the image after block color grading. Block adjustment technology is incorporated into automatic color grading. Using the principles of adjustment, pixel values ​​(RGB) replace coordinate values, and retrograde adjustment calculations, color grading is performed. Workers can select several scene images as color templates (or control images) and use them as a benchmark for color grading. Alternatively, balanced adaptive color grading (i.e., non-block color grading) can be performed, ultimately achieving a uniform and natural color transition at image edges.

[0151] Perform image mosaicking on lidar images that have undergone multi-source image matching, including:

[0152] Perform radiometric correction, geometric correction, registration and overlapping area processing on each lidar image;

[0153] All lidar images are combined and stitched to form a mosaic image without overlapping areas.

[0154] The purpose of geometric mosaicking is to ensure that the geometric positions of corresponding objects between different images are strictly aligned, avoiding obvious misalignment. The images must have a unified projection, coordinate system, spatial resolution, band correspondence, and radiometric properties to ensure that the stitched images maintain consistent geometry and radiometric properties. Furthermore, during the mosaicking process, care must be taken to minimize errors such as discontinuities, duplications, and omissions, ensuring consistent image contrast and similar tones.

[0155] Specifically, image mosaicking includes the following processes: first, image preprocessing, which involves performing radiometric and geometric correction on images to eliminate geometric deviations caused by sensor or platform movement, ensuring that the brightness and geometric shapes between images are consistent; second, image registration, which involves adjusting the spatial positions of each image by selecting control points or using algorithms to automatically match features, so that they are geometrically aligned to eliminate geometric distortion that may occur during the imaging process; third, overlapping area processing, which involves processing overlapping areas when stitching images to ensure a smooth transition and avoid obvious boundaries. Commonly used methods include average color matching and feathering processing; fourth, generating mosaic layers, which involves combining the adjusted images into a seamless mosaic to ensure that the final image has no gaps or overlapping areas.

[0156] Specifically, when mosaicking the same object located in different images, the grayscale difference between the two images can lead to a sudden change. To solve this problem, grayscale mosaicking is used, which mainly includes the following processes: First, geometric correction, which performs geometric correction on each image to put it in a unified coordinate system and ensure that the geometric positions between the images strictly correspond. Second, grayscale adjustment, because different images may have different imaging conditions and time, resulting in inconsistent grayscale values, grayscale adjustment is performed before mosaicking to make the grayscale distribution between the images tend to be consistent. Common methods include histogram configuration, which eliminates grayscale differences by adjusting the mean and variance of the images. Third, edge selection, which selects a suitable edge in the overlapping area that minimizes the grayscale change on both sides. The position of the edge can be determined by calculating the grayscale difference. Fourth, feathering processing, which smoothes the grayscale change on both sides of the edge, reduces the grayscale difference, and makes the stitching more natural. Fifth, mosaicking execution, which uses the mosaicking tool to stitch multiple images into a whole image. During this process, it is necessary to ensure that the grayscale and hue of the image are consistent with the entire image to avoid sudden changes.

[0157] In an optional embodiment, since the original image data is large in size, it may be difficult to load or load slowly when imported into third-party software. Therefore, the image can be split into several smaller images by using image framing output, such as cutting a TIF format image into multiple small TIF format images, thereby improving the efficiency and convenience of data processing. Figure 10The relationship table between the new map sheet number and the sub-frame number is provided. The table records the number of sub-frames of the 1:1 million map sheet at different scales.

[0158] Preferably, when the required remote sensing data is a camera image, preprocessing the camera image includes:

[0159] Perform image correction and format conversion on camera images, including:

[0160] The camera image is rotated according to its declination and tilt angles.

[0161] Among them, drones equipped with camera equipment may produce deviations in attitude angle and heading during flight due to the influence of airflow and wind direction, resulting in excessive rotation and tilt angles of the image. Therefore, the image needs to be properly rotated in the preprocessing stage to eliminate the deviation.

[0162] The camera image is corrected for the principal point according to the center point position of the camera image.

[0163] Among them, the principal point offset refers to the deviation of the position of the image center point, which will affect the spatial geometric relationship of the image. The principal point correction can restore the spatial geometric relationship of the image and ensure the accuracy of the image.

[0164] The camera image is subjected to distortion correction processing according to the radial distortion parameters and tangential distortion parameters of the camera image.

[0165] The cameras mounted on drones are typically non-measuring cameras, and the images they capture are prone to optical distortion, such as barrel or pincushion distortion. This can alter the ground position of the actual scene, so distortion correction is required to eliminate the image distortion caused by lens distortion. Specifically, distortion correction can be achieved by adjusting the radial distortion parameters (k1, k2, k3) and the tangential distortion parameters (p1, p2).

[0166] Perform format conversion on the original data of camera images.

[0167] Among them, in order to facilitate subsequent image processing, the original aerial data needs to be format converted without losing the geometric information and radiation information in the image to ensure the consistency and compatibility of the image data.

[0168] Preferably, preprocessing the camera image further includes:

[0169] Perform triangulation processing on rectified and format-converted camera images, including:

[0170] Extract feature points from camera images and perform matching processing on the feature points;

[0171] Use the matching results of feature points to perform relative and absolute orientation on the camera image;

[0172] Perform adjustment on the oriented camera image.

[0173] After preliminary preprocessing of the camera image, including rotation, principal point correction, distortion correction, and format conversion, additional preprocessing is still required, such as triangulation, orientation, model connection, and point transfer between flight paths. Feature point extraction and matching involves extracting and matching feature points from the image using computer vision methods such as scale-invariant features (SIFT) and Supervised Robust Feature Retrieval (SURF). For example, the SURF algorithm can extract feature points from each image and optimize the matching results using the RANSAC algorithm to improve accuracy.

[0174] refer to Figure 11 The feature point extraction flow chart first reads the image, then initializes the variables that store the feature point data, then extracts the FAST corner points of each affected image, and calculates the BRIEF descriptor of each FAST corner point. Based on the calculated descriptor, the corner points of the two images are matched, and then the corner point pairs with poor matching are filtered out, and finally the matching results are drawn.

[0175] Relative orientation uses image focal length information and matched feature points to perform relative orientation, thereby recovering the spatial pose of each image at the time of capture. Absolute orientation is then performed using control points measured in the field, ensuring that each image has absolute spatial coordinates. Relative orientation uses computer vision methods to match feature points on images to determine the spatial relationship between adjacent images. Specifically, the relative orientation process first extracts feature points from each photo using a feature point detection operator and matches them. The matching results then calculate the orientation coefficients between the image pairs. These orientation coefficients, consisting of a rotation matrix and a translation vector, are used to recover the spatial pose of each image at the time of capture. Absolute orientation builds on relative orientation by integrating the model into a geodetic coordinate system using ground control points (GCPs), thereby achieving absolute spatial positioning of the image. The absolute orientation process typically involves matching the coordinates of known ground control points with the coordinates of corresponding points on the image. The image's exterior elements of orientation (EOPs) are then calculated using optimization methods such as least squares, and the model is ultimately converted to a unified geographic coordinate system.

[0176] Adjustment can be used during the calculation process. Aerial triangulation adjustment methods include bundle block adjustment, which is accomplished by solving for image exterior orientation elements, the three-dimensional coordinates of encrypted points, and the encrypted coordinates of control points. For example, bundle adjustment uses matrix notation to represent the error for a specific image point and linearizes it using a Taylor series expansion. Adjustment accuracy requirements typically include relative orientation accuracy, residual errors at basic orientation points, checkpoint errors, and inter-block common point errors.

[0177] Perform elevation processing on camera images that have been rectified and converted to different formats, including:

[0178] Extracting raw digital surface model data containing height information of the ground and objects above the ground from camera images;

[0179] The digital surface model data is filtered to separate the height information of the ground and objects above the ground, remove non-ground elements, and generate digital elevation model data.

[0180] Among them, feature extraction technology is used to extract digital surface model data (DSM) from the image, and the digital surface model data is filtered to obtain digital elevation model data (DEM). Specifically, stereo image pair matching is first performed. Using image matching technologies such as Match-T, based on the information of the stereo image pair, through feature point matching and mathematical model calculation, the original digital surface model data containing the height information of the ground and objects above the ground (such as buildings, trees, etc.) is automatically extracted. Then, filtering is performed on the extracted digital surface model data to remove noise, smooth the terrain, and separate the height information of objects above the ground. Non-ground elements such as vegetation and buildings are removed, thereby generating digital elevation model data.

[0181] The camera images that have undergone image correction and format conversion are subjected to orthorectification processing, and / or radiometric correction processing, and / or stitching processing.

[0182] Among them, orthorectification is to perform uniform light and color processing on the original image, and then perform digital differential correction on it in combination with the generated DEM. Specifically, the principle of digital differential correction is to realize the geometric relationship between the original image and the corrected image under the premise of the image's orientation parameters, exterior orientation elements and digital elevation model. It is obtained by dividing the image into many small areas, such as an area the size of a pixel, and correcting them one by one, that is, the process of directly using a computer to perform differential correction on the digital image pixel by pixel. Digital differential correction is an image processing technology that uses the interior and exterior orientation elements of the original image and, based on the digital ground model of the correction area, corrects the errors caused by the original image tilt and the projection differences caused by the terrain undulations through digital transformation.

[0183] Radiometric correction converts sensor data into surface reflectance, thereby improving the data's quantitative application capabilities. The main purpose of radiometric correction is to eliminate radiation differences caused by factors such as lighting and atmosphere during the imaging process, ensuring comparability between images acquired at different times or locations. The main radiometric correction methods for camera images include the following:

[0184] Empirical linear calibration, which uses a linear relationship between ground-based data and imagery for radiometric correction, can be used. For example, based on the water reflectance at ground sampling points and the geometrically corrected image spectrum, a linear relationship can be established between the spectral radiometry carried by the drone and the ground-based spectral radiometry. Furthermore, empirical linear calibration demonstrates high accuracy in multispectral image processing, with a coefficient of determination typically greater than 0.9.

[0185] Atmospheric correction and shadow removal: Atmospheric correction is used to compensate for the impact of the atmosphere on image radiation, while shadow removal reduces the impact of shadows on image quality by extracting and transforming shadow areas. For example, methods such as atmospheric contribution, diffuse to direct radiation factors, and empirical line correction can effectively reduce the impact of shadows.

[0186] Geometric correction is combined with radiometric calibration. Geometric correction establishes the relationship between pixels and the ground coordinate system through methods such as band alignment and aerial triangulation, while radiometric calibration performs radiometric correction based on the relationship between the input and output signals of the sensor device. This method can ensure the geometric and radiometric accuracy of the image.

[0187] Radiation block adjustment uses information from overlapping areas between images to calculate globally optimal radiation correction parameters through the optimal path, thereby reducing radiation differences between images. This method is particularly suitable for processing large amounts of image data and can significantly improve the efficiency and accuracy of radiation correction.

[0188] Correction methods based on physical models, such as the empirical linear model (ELM) method combined with an illumination correction factor to calculate surface reflectance. In addition, the effectiveness of the radiation correction model can be verified by setting calibration targets at different grayscale levels.

[0189] Special calibration of multispectral / hyperspectral cameras. For example, the RedEdge multispectral camera uses a global shutter, but there is a conflict between the periodic correction coefficient and the global shutter, which requires correction. In addition, the absolute radiometric calibration of hyperspectral imagers usually uses the reflectivity-based method.

[0190] Automated correction techniques, such as using the SIFT algorithm to match homonymous points and establish grayscale value correlation to correct the radiometric consistency of images. In addition, the quality of relative radiometric calibration can be improved by selecting the best path and using anchor points.

[0191] In a specific embodiment, a method for automatically stitching orthophotos of camera images is provided. This method generates high-precision orthophotos by geometrically correcting, feature matching, and seamlessly stitching multiple images taken by drones. The method is widely used in geographic information mapping, environmental monitoring, disaster assessment, and other fields. The stitching technology steps are as follows:

[0192] Image correction and registration: Images captured by drones often require geometric correction to eliminate geometric errors caused by factors such as flight attitude and camera distortion. Common correction methods include correction based on ground control points (GCPs) and geometric correction without control points. For example, Pix4Dmapper software can quickly correct and stitch camera images with GPS positioning information.

[0193] Feature extraction and matching,In order to achieve accurate registration between images, feature extraction algorithms such as SIFT,SURF, etc. are usually used to identify key points in the image, and feature matching algorithms such as MSAC, RANSAC are used to calculate the transformation matrix between the images.

[0194] Seamless stitching and fusion. After completing the registration, adjacent images need to be seamlessly stitched to eliminate stitching seams and ghosting. Commonly used methods include seam detection based on graph cutting, weighted average fusion, and Poisson fusion.

[0195] Preferably, reference Figure 12 The remote sensing data storage flow chart, S20: all remote sensing data are uniformly connected to the data management system according to the data access rules, including:

[0196] S200: storing the remote sensing data that has been uniformly accessed in a data management system according to data management rules, wherein the data management rules define the types of databases in the data management system and the data storage methods of different types of databases, and the structure of the data and the data storage methods of data with different structures.

[0197] Efficient data storage and rapid access are fundamental requirements for storing and managing multi-source remote sensing data. Due to the differences between different remote sensing data, effective management rules are required to ensure data consistency and accuracy. Data storage methods can include integrated management based on spatial databases and parallel processing using cloud computing platforms such as Hadoop. Additionally, metadata-based approaches can be used to integrate and manage multi-source remote sensing data through data conversion, direct access, and interoperability. Database types and storage methods refer to the selection of different databases for data storage and management based on the data's characteristics and usage requirements. Common databases include relational databases, such as MySQL, suitable for storing structured data; non-relational databases, such as MongoDB, suitable for storing semi-structured or unstructured data; and distributed file systems, suitable for storing large amounts of data. Data structure and storage methods for differently structured data refer to the division of data into structured and semi-structured data, and the subsequent storage in corresponding databases.

[0198] In a specific embodiment, S200: storing the centrally accessed remote sensing data in a data management system according to data management rules includes:

[0199] Connect to a database with multiple distributed storage nodes; at the same time,

[0200] All remote sensing data are divided into blocks to generate data blocks, and the data blocks are stored in databases corresponding to different storage nodes.

[0201] The distributed database utilizes a scalable system architecture, integrating the storage and computing resources of each node in the cluster. By utilizing multiple storage servers to share the storage load and locating storage information, it can meet the requirements for efficient storage and rapid access to spatiotemporal big data. The network distributed file system within the distributed database can disperse remote sensing data across multiple physical servers. The master data server manages and stores remote sensing data, senses the status of the server cluster, and performs scheduling. The data log server backs up the master data server's log files for changes. The data storage server stores remote sensing data in blocks, storing them in blocks when data is inserted.

[0202] In a specific embodiment, S200: storing the remote sensing data accessed in a data management system according to data management rules includes:

[0203] Identifying the data structure of remote sensing data, wherein the data structure includes structured data and semi-structured data;

[0204] Connect the file system to the database with multiple distributed storage nodes;

[0205] The structured remote sensing data is divided into blocks to generate data blocks, and the data blocks are stored in the databases corresponding to different storage nodes;

[0206] Store semi-structured remote sensing data in a file system.

[0207] The file system uses files as a medium to record data and manages the files that contain this data. This approach leverages the advantages of different storage systems to improve storage efficiency by storing structured data in parallel using the parallel framework of a distributed database and semi-structured data using the file system.

[0208] In a specific embodiment, S200: storing the centrally accessed remote sensing data in the data management system according to the data management rules further includes:

[0209] Determine the ID corresponding to each data block;

[0210] Determine the storage location of the data block in the database based on the ID; at the same time,

[0211] Determine the index type of each remote sensing data;

[0212] Insert an index type into the index tree to generate an index entry.

[0213] The remote sensing data is divided into blocks, and the corresponding storage location in the database is determined based on the block data ID. At the same time, the index type of the remote sensing data is inserted into the corresponding index tree data to generate index entries for fast retrieval and access to data. For example, you can select 2D, height, 3D, time, 2D-time, 3D-time, and other index operators to create an index tree.

[0214] In a specific embodiment, referring to Figure 13 The flowchart of data retrieval and access is firstly implemented by using DataCube technology to segment the data, then the remote sensing data format is unified based on ISO standards, and tile spatial identification is established based on GeoSOT to generate spatiotemporal index of remote sensing tiles, which is then indexed and associated through time and satellite + sensor information. Then, the HBase key-value model and hash coding are used to implement efficient retrieval and fast access of remote sensing tile data, and finally, fast retrieval and access of massive multi-source remote sensing data is implemented in a cloud computing environment.

[0215] Preferably, before preprocessing the remote sensing data, the method further comprises:

[0216] Perform coordinate transformation on all remote sensing data to unify the coordinate system of remote sensing data;

[0217] Scale conversion is performed on all remote sensing data to unify the scale of the remote sensing data, wherein the scale includes resolution and / or scale.

[0218] This method can be performed before unified data access or data preprocessing. Scale includes standards for resolution and scale, ensuring consistency in resolution and scale across different remote sensing data. The coordinate system provides a unified spatial framework, ensuring that all remote sensing data can be compared and integrated within the same spatial reference system.

[0219] Example 2

[0220] The second embodiment of the present application provides a multi-source remote sensing data processing device based on the first embodiment, referring to Figure 14 A schematic diagram of a multi-source remote sensing data processing device is provided, comprising: a parsing module for parsing uploaded data from different remote sensing devices to obtain remote sensing data; an access module for uniformly accessing all the remote sensing data into a data management system according to data access rules, wherein the data access rules define at least one of a data format, a data encoding rule, and a data naming rule; a processing module for retrieving required remote sensing data from the data management system according to data requirements and preprocessing the remote sensing data; and a fusion module for fusing the preprocessed remote sensing data to generate a fused data set.

[0221] Among them, data access rules are formulated to ensure that all remote sensing data are accessed according to unified standards, which solves the problem of inconsistent formats and incompatibility of data due to diverse sources, improves the standardization of data, and integrates remote sensing data into a unified data set, further enhancing data consistency and facilitating sharing and application between different platforms and systems.

[0222] Example 3

[0223] Embodiment 3 of the present application provides a computer device, including a memory and a processor; the memory stores a computer program that can be run on the processor, and when the computer program is executed by the processor, the multi-source remote sensing data processing method provided in the above embodiment 1 is executed.

[0224] Among them, reference Figure 15, which exemplarily illustrates the computer device of this embodiment, may include a processor 1510, a video display adapter 1511, a disk drive 1512, an input / output interface 1513, a network interface 1514, and a memory 1520. The processor 1510, video display adapter 1511, disk drive 1512, input / output interface 1513, network interface 1514, and memory 1520 may be communicatively connected via a communication bus 1530.

[0225] Among them, the processor 1510 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in this application.

[0226] The memory 1520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1520 can store an operating system 1521 for controlling the operation of the computer device and a basic input and output system 1522 for controlling the low-level operation of the computer device. In addition, a web browser 1523, a data storage management system 1524, and a device identification information processing system 1525, etc. can also be stored. The above-mentioned device identification information processing system 1525 can be an application program that specifically implements the operations of the aforementioned steps in the embodiment of the present application. In short, when the technical solution provided by the present application is implemented by software or firmware, the relevant program code is stored in the memory 1520 and is called and executed by the processor 1510.

[0227] The input / output interface 1513 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0228] The network interface 1514 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WIFI, Bluetooth, etc.).

[0229] The communication bus 1530 comprises a pathway for transmitting information between the various components of the device (eg, the processor 1510 , the video display adapter 1511 , the disk drive 1512 , the input / output interface 1513 , the network interface 1514 , and the memory 1520 ).

[0230] In addition, the device can also obtain information on specific collection conditions from a virtual resource object collection condition information database for use in condition judgment, and so on.

[0231] It should be noted that although the above device only shows a processor 1510, a video display adapter 1511, a disk drive 1512, an input / output interface 1513, a network interface 1514, a memory 1520, a communication bus 1530, etc., in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may also include only the components necessary to implement the solution of the present application, and does not necessarily include all the components shown in the figure.

[0232] Example 4

[0233] A fourth embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed, the multi-source remote sensing data processing method provided in the first embodiment above is implemented.

[0234] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application or certain parts of the embodiments.

[0235] The above is a detailed introduction to the technical solutions provided by this application. Specific examples are used herein to illustrate the principles and implementation methods of this application. The description of the above embodiments is intended only to help understand the method and core concept of this application. At the same time, those skilled in the art will appreciate that variations in the specific implementation methods and scope of application are possible based on the concepts of this application. In summary, the contents of this specification should not be construed as limiting this application.

Claims

1. A multi-source remote sensing data processing method, characterized in that: The method comprises: Parse the uploaded data of different remote sensing devices to obtain remote sensing data; All the remote sensing data are uniformly accessed into a data management system according to data access rules, wherein the data access rules define at least one of a data format, a data encoding rule, and a data naming rule; Retrieving required remote sensing data from the data management system according to data requirements and preprocessing the remote sensing data; The pre-processed remote sensing data is fused to generate a fused data set.

2. The multi-source remote sensing data processing method according to claim 1, characterized in that: The method of parsing uploaded data of different remote sensing devices to obtain remote sensing data includes: receiving and parsing data uploaded by a synthetic aperture radar device to obtain synthetic aperture radar images therefrom; and / or, Receive and parse data uploaded by a LiDAR device to obtain LiDAR images; and / or, Receive and parse the data uploaded by the camera device to obtain camera images.

3. The multi-source remote sensing data processing method according to claim 2, characterized in that: When the required remote sensing data is a synthetic aperture radar image, preprocessing the synthetic aperture radar image includes: Performing radiometric correction on the synthetic aperture radar image, including: Correction of synthetic aperture radar image calibration coefficients; Converting the complex format data in the synthetic aperture radar image into amplitude data based on the corrected synthetic aperture radar image calibration coefficient; and / or, Performing multi-view processing on the synthetic aperture radar image includes: Dividing the effective synthetic aperture length of the synthetic aperture radar image into multiple segments, each segment imaging the same scene to obtain corresponding image data; summing and superimposing all imaged image data to generate a multi-view synthetic aperture radar image; and / or, Performing geometric processing on the synthetic aperture radar image, including: Performing slant-to-ground range conversion and geocoding on the synthetic aperture radar image, wherein the geocoding includes coding ellipsoid correction and geocoding terrain correction; and / or, Perform filtering processing on the synthetic aperture radar image.

4. The multi-source remote sensing data processing method according to claim 2, characterized in that: When the required remote sensing data is a lidar image, preprocessing the lidar image includes: Performing multi-source image matching on the laser radar image includes: Identify key points between at least two lidar images for image matching.

5. The multi-source remote sensing data processing method according to claim 4, characterized in that: Preprocessing the laser radar image further includes: Performing block adjustment on the lidar image after multi-source image matching; and / or, Performing orthorectification on the lidar image after multi-source image matching, including: Converting oblique LIDAR images into orthophotos; and / or, Performing color grading on the laser radar image after multi-source image matching, wherein the color grading includes at least one of template color grading, geographic template color grading, and regional network color grading; and / or, Performing image mosaicking on the laser radar image after multi-source image matching, including: Performing radiation correction, geometric correction, registration and overlapping area processing on each of the laser radar images; All of the laser radar images are combined and spliced ​​to form a mosaic image without overlapping areas.

6. The multi-source remote sensing data processing method according to claim 2, characterized in that: When the required remote sensing data is a camera image, preprocessing the camera image includes: Performing image correction and format conversion on the camera image, including: Rotating the camera image according to the yaw angle and tilt angle of the camera image; and / or, Performing principal point correction processing on the camera image according to the center point position of the camera image; and / or, performing distortion correction processing on the camera image according to the radial distortion parameter and the tangential distortion parameter of the camera image; Perform format conversion processing on the original data of the camera image.

7. The multi-source remote sensing data processing method according to claim 6, characterized in that: Preprocessing the camera image further includes: Performing triangulation processing on the camera image after image correction and format conversion, including: Extracting feature points from the camera image and performing matching processing on the feature points; Performing relative and absolute orientation on the camera image using the matching results of the feature points; performing adjustment on the oriented camera image; and / or, Performing elevation processing on the camera image that has undergone image correction and format conversion, including: extracting raw digital surface model data including height information of the ground and objects above the ground from the camera image; Filtering the digital surface model data to separate the height information of the ground and objects above the ground, removing non-ground elements, and generating digital elevation model data; and / or, The camera image that has undergone image correction and format conversion is subjected to orthorectification processing, and / or radiometric correction processing, and / or stitching processing.

8. The multi-source remote sensing data processing method according to claim 1, characterized in that: The step of uniformly accessing all the remote sensing data to the data management system according to the data access rules further includes: The remote sensing data that has been uniformly accessed is stored in a data management system according to data management rules, wherein the data management rules define the types of databases in the data management system and the data storage methods of different types of databases, the structure of the data and the storage methods of data with different structures.

9. The multi-source remote sensing data processing method according to claim 8, characterized in that: Storing the remote sensing data accessed in a unified manner in a data management system according to data management rules includes: Identifying a data structure of the remote sensing data, wherein the data structure includes structured data and semi-structured data; Connect the file system to the database with multiple distributed storage nodes; Dividing the structured remote sensing data into blocks to generate data blocks, and storing the data blocks in databases corresponding to different storage nodes; The semi-structured remote sensing data is stored in the file system.

10. The multi-source remote sensing data processing method according to claim 1, characterized in that: Before preprocessing the remote sensing data, the method further includes: Performing coordinate transformation on all the remote sensing data to unify the coordinate system of the remote sensing data; Scale conversion is performed on all the remote sensing data to unify the scales of the remote sensing data, wherein the scale includes resolution and / or scale.

11. A multi-source remote sensing data processing device, characterized in that: include: Parsing module, used to parse the uploaded data of different remote sensing devices to obtain remote sensing data; An access module, configured to uniformly access all of the remote sensing data into a data management system according to data access rules, wherein the data access rules define at least one of a data format, a data encoding rule, and a data naming rule; A processing module, configured to retrieve required remote sensing data from the data management system according to data requirements and pre-process the remote sensing data; The fusion module is used to perform fusion processing on the pre-processed remote sensing data to generate a fused data set.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

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