Method and system for rapidly analyzing mass remote sensing images

Abstract

The invention relates to the technical field of mass remote sensing image data analysis, in particular to a mass remote sensing image rapid analysis method and system.The method comprises the steps that metadata files of remote sensing image data are read in batches, the coordinate range and key attribute information of each scene of data are rapidly extracted from the metadata files, and vector range files of mass remote sensing images are generated; then calculating the down-sampling resolution and the image coordinate range of the thumbnail of the remote sensing image, calculating the affine transformation coefficient of the thumbnail according to the down-sampling resolution and the image coordinate range of the thumbnail, and endowing coordinate information to the thumbnail; and finally, quickly browsing massive remote sensing images in a geographic information system by opening the vector range file to overlay the thumbnail, and accurately analyzing the remote sensing images by using key attribute information of the vector range file. Vector range files with complete attribute information and corresponding image thumbnail files with coordinate information are rapidly generated in batches for massive remote sensing images, and the analysis efficiency and accuracy are higher.

Description

Technical Field

[0001] The present invention relates to the technical field of massive remote sensing image data analysis, and in particular to a method and system for rapid analysis of massive remote sensing images. Background Art

[0002] At present, the establishment of massive remote sensing image mosaic datasets consumes a lot of memory and time, and the operation is cumbersome and inefficient, resulting in the separation of remote sensing image vector range (drop map) files from image data, complex dynamic updates, and a large amount of attribute information missing in drop map files, making it difficult to analyze massive image data, and unable to get rid of the dependence on massive image datasets and quickly and accurately determine the coverage and loophole areas of image data. Remote sensing image thumbnails have no coordinate information, the system cannot identify the geographical area corresponding to the thumbnail, and cannot be displayed according to the correct geographical location. When browsing in batches, multiple images will be superimposed and displayed in the upper left corner of the software interface, making it impossible to quickly browse and analyze images, affecting the cloud judgment of image data, initial screening of image quality, and initial accuracy detection. In addition, many geographic information system (GIS) analysis functions, such as terrain analysis, spatial interpolation, buffer analysis, etc., need to be calculated based on an accurate coordinate system. Images without coordinate information will make it impossible to directly carry out analysis tasks, or the results obtained will be inaccurate and meaningless, affecting the in-depth mining and understanding of remote sensing image data. Therefore, the map files cannot be updated quickly and the thumbnails lack coordinate information, which makes it impossible to quickly browse and analyze remote sensing images in batches in the geographic information system, seriously affecting the processing efficiency of massive remote sensing images and the planning and design of massive remote sensing image tasks. Summary of the invention

[0003] The purpose of the present invention is to solve the problems existing in the prior art and to propose a method and system for rapid analysis of massive remote sensing images, which can rapidly generate vector range (drop map) files (.shp) with complete attribute information and corresponding image thumbnail files (.jgw) with coordinate information for massive remote sensing images in batches, so as to realize rapid batch browsing and higher analysis accuracy.

[0004] To achieve the above purpose, the technical solution adopted is:

[0005] A method for rapid analysis of massive remote sensing images, including:

[0006] First, the metadata files of remote sensing image data are read in batches, and the coordinate range and key attribute information of each scene data are quickly extracted from the metadata files to generate vector range files of massive remote sensing images;

[0007] Then, the downsampling resolution and the image coordinate range of the remote sensing image thumbnail are calculated, and the affine transformation coefficient of the thumbnail is calculated according to the obtained downsampling resolution and the image coordinate range of the thumbnail, so as to give the coordinate information to the thumbnail;

[0008] Finally, in the geographic information system, a large amount of remote sensing images can be quickly browsed by opening the vector range file and overlaying thumbnails, and the key attribute information of the vector range file can be used to accurately analyze the remote sensing images.

[0009] According to the method for rapid analysis of massive remote sensing images of the present invention, further, the rapid extraction of the coordinate range and key attribute information of each scene data from the metadata file includes: by parsing the metadata file, obtaining the coordinates of the upper left corner and the lower right corner and the key attribute information of each scene data, assuming that the coordinates of the upper left corner are (x 1 ,y 1 ), the coordinate of the lower right corner is (x 2 ,y 2 ), the coordinate range of the image data is expressed as:

[0010]

[0011] Among them, (x, y) represents the coordinates of a pixel point in the image data;

[0012] The key attribute information includes the number of bands, horizontal resolution, vertical resolution and sampling rate.

[0013] According to the method for rapid analysis of massive remote sensing images of the present invention, further, the calculation of the downsampling resolution of the remote sensing image thumbnail includes: calculating the horizontal and vertical ground resolution of the thumbnail, i.e., the downsampling resolution, according to the geographic coordinates of the four corners of the original remote sensing image and the size of the thumbnail, and the calculation formula is as follows:

[0014] Assume that the geographic coordinates of the four corners of the original remote sensing image are the upper left corner (X UpperLeft , Y UpperLeft ), upper right corner (X UpperRight , Y UpperRight ), lower left corner (X BottomLeft , Y BottomLeft ), lower right corner (X BottomRight , Y BottomRight ); the width and height of the thumbnail are W bro and H bro , then the horizontal and vertical ground resolution R after thumbnail downsampling x_bro and R y_bro The calculation is as follows:

[0015]

[0016] According to the method for rapid analysis of massive remote sensing images of the present invention, further, the calculating of the image coordinate range of the remote sensing image thumbnail comprises: calculating the image coordinate range of the thumbnail according to the coordinate range of the original remote sensing image, the size of the thumbnail and the downsampling resolution, and the calculation process is:

[0017] Assume that the pixel coordinates of the four corners of the thumbnail are the upper left corner (x UpperLeft ,y UpperLeft ), upper right corner (x UpperRight ,y UpperRight ), lower left corner (x BottomLeft ,y BottomLeft ), lower right corner (x BottomRight ,y BottomRight ), then:

[0018] (x UpperLeft ,y UpperLeft )=(0,0)

[0019] (x UpperRight ,y UpperRight )=(W bro ,0)

[0020] (x BottomLeft ,y BottomLeft )=(0,H bro )

[0021] (x BottomRight ,y BottomRight 0=(W bro , H bro )

[0022] Assume that the geographic coordinates of the four corners of the thumbnail are the upper left corner (XB UpperLeft , Y.B. UpperLeft ), upper right corner (XB UpperRight , Y.B. UpperRight ), lower left corner (XB BottomLeft , Y.B. BottomLeft ), lower right corner (XB bottomRight , Y.B. BottomRight ), the image coordinate range of the thumbnail is:

[0023] (XB UpperLeft , Y.B. UpperLeft 0=(X UpperLeft , Y UpperLeft )

[0024]

[0025] (XB BottomLeft , Y.B. BottomLeft )=(X UpperLeft , Y UpperLeft-R y_bro ×H bro )

[0026]

[0027] According to the method for rapid analysis of massive remote sensing images of the present invention, further, calculating the affine transformation coefficient of the thumbnail includes:

[0028] First, assume that a point (x, y) in the thumbnail image is mapped to the geographic coordinate system (X, Y) after affine transformation. The affine transformation relationship is expressed as:

[0029]

[0030] Among them, the matrix represents the linear transformation part, Represents the translation part, xˊ is the geographic horizontal coordinate corresponding to the pixel, yˊ is the geographic vertical coordinate corresponding to the pixel, x is the pixel horizontal coordinate, y is the pixel vertical coordinate, a is the pixel resolution in the X direction, c and b are the scaling and rotation coefficients, d is the pixel resolution in the Y direction, e is the X coordinate of the pixel center in the upper left corner of the raster map, and f is the Y coordinate of the pixel center in the upper left corner of the raster map;

[0031] Then substitute the pixel coordinates and geographic coordinates of the four corners of the thumbnail image into the affine transformation relationship calculation formula to obtain the affine transformation coefficient. The affine transformation relationship calculation formula can be used to calculate the geographic coordinate information of any point in the thumbnail.

[0032] According to the method for rapid analysis of massive remote sensing images of the present invention, further, the four-corner pixel coordinates and geographic coordinates of the thumbnail image are substituted into the affine transformation relationship calculation formula to calculate the affine transformation coefficients, which include:

[0033] The pixel coordinates of the upper left corner of the thumbnail (x UpperLeft ,y UppetLeft ) that is (0, 0) and the corresponding geographic coordinates (XB UpperLeft , Y.B. UpperLeft ) That is (X UpperLeft , Y UpperLeft ) into the affine transformation relationship calculation formula, we get:

[0034] e=X UpperLeft

[0035] f=Y UpperLeft

[0036] The pixel coordinates of the upper right corner of the thumbnail (x UpperRight ,y UpperRigjt ) That is (x UpperRight ,0) and the corresponding geographic coordinates (XB UpperRight , Y.B.UpperRight ) into the affine transformation relationship calculation formula, we get:

[0037]

[0038] Set the pixel coordinates of the lower left corner of the thumbnail (x BottomLeft ,y BottomLeft ) that is (0, y BottomLeft ) and the corresponding geographic coordinates (XB BottomLeft , Y.B. BottomLeft ) into the affine transformation relationship calculation formula, we get:

[0039]

[0040] According to the method for rapid analysis of massive remote sensing images of the present invention, further, the geographic information system adopts GlobalMapper, ArcGIS, QGIS or MapInfo.

[0041] Furthermore, the present invention also provides a system for rapid analysis of massive remote sensing images, which is used to implement the method for rapid analysis of massive remote sensing images as described above, and the system comprises:

[0042] Vector range file generation module, which is used to batch read metadata files of remote sensing image data, quickly extract the coordinate range and key attribute information of each scene data from the metadata file, and generate vector range files of massive remote sensing images;

[0043] The thumbnail coordinate information generation module is used to calculate the downsampling resolution and image coordinate range of the remote sensing image thumbnail, calculate the affine transformation coefficient of the thumbnail according to the obtained downsampling resolution and image coordinate range of the thumbnail, and assign coordinate information to the thumbnail;

[0044] The analysis module is used to quickly browse massive remote sensing images in the geographic information system by opening vector range files and overlaying thumbnails, and to accurately analyze remote sensing images using key attribute information of vector range files.

[0045] The beneficial effects achieved by adopting the above technical solution are:

[0046] (1) Improved efficiency in analyzing massive remote sensing images. Compared with mainstream methods, remote sensing image thumbnails in this method have coordinate information, which enables rapid processing, batch opening, and efficient browsing of massive remote sensing image data, resulting in higher analysis efficiency.

[0047] (2) Stronger analysis capabilities for massive remote sensing images. Compared with the vector range (drop map) files generated by mainstream methods, this method has complete attribute information, can provide more complete metadata information for massive remote sensing image analysis, and provide more favorable support for various data analysis and information mining.

[0048] (3) Massive remote sensing image analysis is more lightweight. This method is based on metadata files and thumbnails, which has lower requirements on device storage and performance, making the analysis of massive data independent of large volumes of data and making the application more lightweight. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings of the embodiments of the present invention, wherein the drawings are only used to illustrate some embodiments of the present invention, but not to limit all embodiments of the present invention thereto.

[0050] Figure 1 is a schematic diagram of a process of a method for rapid analysis of massive remote sensing images according to an embodiment of the present invention;

[0051] Figure 2 is a process diagram of generating a vector range file according to an embodiment of the present invention;

[0052] Figure 3 is a schematic diagram of a pixel coordinate system and a geographic coordinate system according to an embodiment of the present invention;

[0053] Figure 4 It is the operation interface of the self-compiled program of the method for rapid analysis of massive remote sensing images according to the embodiment of the present invention;

[0054] Figure 5 The output drop map file and satellite image thumbnail are opened in ArcMap according to an embodiment of the present invention;

[0055] Figure 6 The present invention is an embodiment of opening a satellite image thumbnail without coordinates in ArcMap. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings of specific embodiments of the present invention to clearly and completely describe the exemplary scheme of the embodiment of the present invention. Unless otherwise defined, the technical terms or scientific terms used in the present invention should be the common meanings understood by people with ordinary skills in the field.

[0057] This solution focuses on solving the problem of rapid analysis of massive remote sensing image data. Relying on the advantages of complete attribute information of remote sensing image metadata and thumbnail files, small space occupation, lightweight and high efficiency, based on existing remote sensing image metadata and thumbnail files, by programming and calling ArcPy and GDAL libraries, batches of remote sensing image vector range (drop map) files (.shp) with complete attribute information and their corresponding thumbnail files (.jgw) with coordinate information are quickly generated. These files can provide strong support for the data analysis of massive remote sensing images, making the analysis process more efficient and accurate.

[0058] like Figure 1 As shown, this embodiment discloses a method for rapid analysis of massive remote sensing images, which includes the following contents:

[0059] Step S1: For remote sensing image data sets, each image data has a corresponding metadata file. It is necessary to batch read the metadata files of remote sensing image data, quickly extract the coordinate range and key attribute information of each scene data from the metadata files, and generate vector range files of massive remote sensing images. Specifically including steps S101-S104:

[0060] Step S101: Read metadata file

[0061] First, you need to write a program to batch read the metadata files of remote sensing image data, which may involve file path management, metadata format parsing, etc. Retrieve metadata files from the folder. If you need to generate coordinate information, you also need to read the corresponding thumbnail files and store them in the ArrayList list for subsequent batch processing.

[0062] Step S102: Parsing metadata

[0063] After reading the metadata file, you need to parse the key information, such as geographic reference information, number of bands, resolution, etc. This information is usually stored in XML, JSON and other formats, so you need to parse it line by line and extract relevant data. Because different satellites organize their metadata formats differently, when extracting attribute information, you need to combine the type of satellite imagery and parse it from different node positions. Some also need to be analyzed to extract some bytes.

[0064] Step S103: Extract coordinate range and key attribute information

[0065] By parsing the metadata file, we can obtain the coordinates of the upper left corner and lower right corner of each scene data, as well as key attribute information such as the number of bands and resolution. Assume that the coordinates of the upper left corner are (x 1 ,y 1 ), the coordinate of the lower right corner is (x 2 ,y 2 ), the coordinate range of the image data can be expressed as:

[0066]

[0067] Among them, (x, y) represents the coordinates of a pixel point in the image data.

[0068] Key attribute information includes the number of bands b, horizontal resolution rx, vertical resolution ry and sampling rate s.

[0069] Number of bands b: represents the number of different frequency bands contained in the image data.

[0070] Horizontal resolution rx and vertical resolution ry: indicate the actual length or area represented by each pixel in the horizontal and vertical directions.

[0071] Sampling rate s: represents the sampling frequency of remote sensing image data.

[0072] Step S104: Generate vector range file

[0073] Based on the extracted coordinate range information, it can be organized and stored as a vector range file (.shp), which contains the spatial range information of the image data and can be used for further spatial analysis and processing.

[0074] In summary, in order to batch read the metadata files of remote sensing image data and quickly extract the coordinate range and key attribute information of each scene data, some commonly used computer vision tools (such as GDAL, OpenCV, etc.) are needed to complete data reading, parsing, conversion and other operations. The process of finally generating vector range files of massive remote sensing images is as follows: Figure 2 As shown, in Figure 2 In the process, each step is connected in sequence. Starting from the start node A, the process goes through reading metadata file B, parsing metadata C, extracting coordinate range and key attribute information D, and finally generating vector range file E, and finally reaching the end node F.

[0075] Step S2, calculating the downsampling resolution and image coordinate range of the remote sensing image thumbnail, calculating the affine transformation coefficient of the thumbnail according to the obtained downsampling resolution and image coordinate range of the thumbnail, and assigning coordinate information to the thumbnail. Specifically including steps S201-S203.

[0076] Step S201: Calculate the downsampling resolution of the thumbnail

[0077] The downsampling of thumbnails can be achieved through resampling methods, commonly used methods include nearest neighbor interpolation, bilinear interpolation, cubic convolution interpolation, etc. According to the geographic coordinates of the four corners of the original remote sensing image and the size of the thumbnail, the horizontal and vertical ground resolution of the thumbnail, that is, the downsampling resolution, can be calculated. The calculation formula is as follows:

[0078] Assume that the geographic coordinates of the four corners of the original remote sensing image are the upper left corner (X UpperLeft , Y UpperLeft ), upper right corner (X UpperRight , Y UpperRight ), lower left corner (X BottomLeft , Y BottomLeft ), lower right corner (X BottomRight , Y BottomRight ); the width and height of the thumbnail are W bro and Hbro , then the horizontal and vertical ground resolution R after thumbnail downsampling x_bro and R y_bro The calculation is as follows:

[0079]

[0080] Step S202: Calculate the image coordinate range of the thumbnail

[0081] According to the coordinate range of the original remote sensing image, the size of the thumbnail and the downsampling resolution, the image coordinate range of the thumbnail can be calculated. Figure 3 For pixel coordinate system and geographic coordinate system, the calculation process is:

[0082] Assume that the pixel coordinates of the four corners of the thumbnail are the upper left corner (x UpperLeft ,y UpperLeft ), upper right corner (x UpperRight ,y UpperRight ), lower left corner (x BottomLeft ,y BottomLeft ), lower right corner (x BottomRight ,y BottomRight ), then:

[0083] (x UpperLeft ,y UpperLeft )=(0,0)

[0084] (x UpperRight ,y UpperRight )=(W bro ,0)

[0085] (x BottomLeft , t BottomLeft )=(0,H bro )

[0086] (x BottomRight , t BottomRight )=(W bro , H bro )

[0087] Assume that the geographic coordinates of the four corners of the thumbnail are the upper left corner (XB UpperLeft , Y.B. UpperLeft ), upper right corner (XB UpperRight , Y.B. UpperRight ), lower left corner (XB BottomLeft , Y.B. BottomLeft ), lower right corner (XB BottomRight , Y.B. BottomRight ), the image coordinate range of the thumbnail is:

[0088] (XB UpperLeft , Y.B. UpperLeft)=(X UpperLeft , Y UpperLeft )

[0089]

[0090] (XB BottomLeft , Y.B. BottomLeft )=(X UpperLeft , Y UpperLeft -R y_bro ×H bro )

[0091]

[0092] Step S203: Calculate the affine transformation coefficient of the thumbnail

[0093] The affine transformation coefficients of the thumbnail include rotation and scaling coefficients, which can be obtained by calculating the affine relationship between the pixel coordinates of the image thumbnail and the actual geographic coordinates.

[0094] Assuming that a point (x, y) in the thumbnail image is mapped to the geographic coordinate system (X, Y) after affine transformation, the affine transformation relationship is expressed as:

[0095]

[0096] Among them, the matrix represents the linear transformation part, Represents the translation part, xˊ is the geographic horizontal coordinate corresponding to the pixel, yˊ is the geographic vertical coordinate corresponding to the pixel, x is the pixel horizontal coordinate, y is the pixel vertical coordinate, a is the pixel resolution in the X direction, c and b are the scaling and rotation coefficients, d is the pixel resolution in the Y direction, e is the X coordinate of the pixel center in the upper left corner of the raster map, and f is the Y coordinate of the pixel center in the upper left corner of the raster map.

[0097] Then substitute the pixel coordinates and geographic coordinates of the four corners of the thumbnail image into the affine transformation relationship calculation formula to calculate the affine transformation coefficient, and then use the affine transformation relationship calculation formula to calculate the geographic coordinate information of any point in the thumbnail. The details are as follows:

[0098] The pixel coordinates of the upper left corner of the thumbnail (x UpperLeft ,y UpperLeft ) that is (0, 0) and the corresponding geographic coordinates (XB UpperLeft , Y.B. UpperLeft ) That is (X UpperLeft , Y UpperLeft ) into the affine transformation relationship calculation formula, we get:

[0099] e=X UppetLeft

[0100] f=YUpperLeft

[0101] The pixel coordinates of the upper right corner of the thumbnail (x UpperRight ,y UpperRight ) That is (x UpperRight ,0) and the corresponding geographic coordinates (XB UpperRight , Y.B. UpperRight ) into the affine transformation relationship calculation formula, we get:

[0102]

[0103] Set the pixel coordinates of the lower left corner of the thumbnail (x BottomLeft ,y BottomLeft ) that is (0, y BottomLeft ) and the corresponding geographic coordinates (XB BottomLeft , Y.B. BottomLeft ) into the affine transformation relationship calculation formula, we get:

[0104]

[0105] Abbreviations in the above formula Figure 4 The pixel coordinates of the corners can be obtained by the number of rows and columns of the image, i.e., W bro and H bro The geographic coordinates can be obtained through the four-corner coordinates recorded in the original image data coordinate file, calculated through the pixel resolution and the number of rows and columns of the image data, which can be used as known quantities for calculating the affine transformation coefficients.

[0106] matrix It controls the coordinate mapping relationship in the thumbnail, that is, it affects the rotation, scaling and shearing transformation effects in the thumbnail.

[0107] vector Controls the translation effect in the thumbnail, that is, affects the position of the thumbnail in the original image.

[0108] Step S3, in the geographic information system, a large amount of remote sensing images are quickly browsed by opening the vector range file and superimposing thumbnails, and the key attribute information of the vector range file is used to accurately analyze the remote sensing images. Specifically, steps S301 and S302 are included.

[0109] Step S301, open the map file in the geographic information system and overlay the thumbnail.

[0110] GlobalMapper is a software system specially used for processing, analyzing and displaying geospatial data. In this software, you can open the map file and quickly browse the massive remote sensing images in batches by overlaying thumbnails. Other compatible geographic information system analysis software include: ArcGIS, QGIS, MapInfo, etc. The attribute information that can be read includes: After opening the map file attribute information, you can obtain various attribute information of the remote sensing image, such as coordinate system, resolution, number of bands, pixel type, cloud cover, data quality, etc.

[0111] Step S302: Accurately analyze the remote sensing image

[0112] Using geographic information systems, remote sensing images can be accurately analyzed in many aspects, such as terrain analysis, land use / cover classification, water resource analysis, ecological environment evaluation, urban planning analysis, etc. The application of analysis results is: through accurate analysis of remote sensing images, it can provide support for decision-making in various fields, such as land planning, resource management, environmental protection, disaster prevention, transportation planning, etc. At the same time, the analysis results of remote sensing images can also be used for mapping, information extraction and data updating.

[0113] Corresponding to the above method, this embodiment also discloses a system for rapid analysis of massive remote sensing images, the system comprising:

[0114] The vector range file generation module is used to batch read the metadata files of remote sensing image data, quickly extract the coordinate range and key attribute information of each scene data from the metadata files, and generate vector range files for massive remote sensing images.

[0115] The thumbnail coordinate information generation module is used to calculate the downsampling resolution and image coordinate range of the remote sensing image thumbnail, calculate the affine transformation coefficient of the thumbnail according to the obtained downsampling resolution and image coordinate range of the thumbnail, and assign coordinate information to the thumbnail.

[0116] The analysis module is used to quickly browse massive remote sensing images in the geographic information system by opening vector range files and overlaying thumbnails, and to accurately analyze remote sensing images using key attribute information of vector range files.

[0117] In order to verify the effectiveness of this scheme, further explanation is given below in combination with experimental data.

[0118] (1) Experimental environment

[0119] The experimental operating system is Windows 10, and the development environment is VisualStudio 2010 C#, ArcPy 2.7 and GDAL 2.2.4.

[0120] (2) Experimental data

[0121] This experiment mainly analyzes the remote sensing satellite image data of three types of domestic independent satellites, namely, Ziyuan-3, Gaofen-7 and Gaofen-3, which are meter-level, sub-meter-level optical satellites and SAR satellites respectively. The metadata file (.XML) and thumbnail file (.JPG) used in this experiment are 2.87MB in size. In the conventional method, the 1B-level data of the remote sensing satellite image of Ziyuan-3 01, its three-line array panchromatic image (front and rear resolution 3.5 meters, downward resolution 2.1 meters) and multispectral image (resolution 5.8 meters) are a total of 2.8GB. The present invention realizes the rapid analysis and processing of massive 1B-level remote sensing satellite images through small sample data.

[0122] (3) Operation process

[0123] The operation interface of the self-programmed program of this method is as follows Figure 4 As shown in the figure, select the input directory, output directory, and satellite image type in the operation interface, and click "Generate drop image file" and "Generate thumbnail coordinate file" in turn to generate drop image file and thumbnail coordinate file in the output directory. Copy the thumbnail image file to the output directory, and after loading it in ArcMap, the interface for image analysis is as follows: Figure 5 As shown ( Figure 5 The bottom table shows the geographic coordinates and attribute information). This interface also loads the remote sensing image drop file and thumbnail file for analysis, as follows: ① The red border in the figure is the vector range of each image (.shp drop map), which is a polygon shp file generated by the corner coordinates in the image metadata file (.xml). Through the shp file, the coverage and overlap of the massive image data in the mission area can be analyzed by only opening the drop map; ② The attribute file at the bottom of the figure contains complete attribute information of the original image, such as sensor type, orbit number, photography date, pendulum angle, solar altitude angle, image corner coordinates and cloud coverage of the initial screening of 1B level products. This file can be used to support attribute-based retrieval and analysis; ③ The image in the figure is a thumbnail image with coordinate information. The preview function of the geographic information system such as ArcMap can display the image in the main interface according to the corresponding coordinate range. The thumbnail images in the mission area can be loaded in batches at the same time, so as to realize the rapid viewing and analysis of image quality, initial positioning accuracy, terrain type and cloud coverage.

[0124] (4) Comparison with mainstream methods

[0125] 1. Supports retrieval based on attribute information and cloud screening and other analyses. Currently, mainstream satellite remote sensing image production software such as GXL and EasySAT all have the function of generating image drop files, but the attribute items of the drop files they generate only have the image name and lack other attribute information, which cannot meet the needs of remote sensing image analysis in actual production; this method writes the attribute information in the metadata into the drop file, and supports retrieval based on attribute information and cloud screening and other analyses.

[0126] 2. Thumbnails with coordinate information can be generated. Currently, all 1B-level remote sensing satellite image data results only have thumbnails with images, but no coordinate information. They cannot be displayed in the correct position in the geographic information system. Multiple images are superimposed and displayed in the upper left corner, making it impossible to browse and analyze images. After this method generates the coordinate information file, it can be quickly browsed in batches in the software (as shown in the figure). Figure 5 ), Figure 6 This is the effect of opening a thumbnail without coordinate information.

[0127] 3. It realizes lightweight analysis of massive remote sensing image data. In current remote sensing satellite image processing software, such as ArcMap, the mosaic data set generated for analysis must read the original image data. The analysis of massive remote sensing image data depends on massive data, and the efficiency of forward dataset construction is low, and it is highly dependent on device performance and storage. This method is only based on metadata files and downsampled thumbnails, and only needs to read 1 / 10 of the original data, which realizes lightweight analysis of massive remote sensing image data.

[0128] Unless otherwise specifically stated, the components, steps, numerical expressions and values ​​set forth in these embodiments do not limit the scope of the present invention.

[0129] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0130] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0131] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0132] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for rapid analysis of massive remote sensing images, characterized in that: Include: First, the metadata files of remote sensing image data are read in batches, and the coordinate range and key attribute information of each scene data are quickly extracted from the metadata files to generate vector range files of massive remote sensing images; Then, the downsampling resolution and the image coordinate range of the remote sensing image thumbnail are calculated, and the affine transformation coefficient of the thumbnail is calculated according to the obtained downsampling resolution and the image coordinate range of the thumbnail, so as to give the coordinate information to the thumbnail; Finally, in the geographic information system, a large amount of remote sensing images can be quickly browsed by opening the vector range file and overlaying thumbnails, and the key attribute information of the vector range file can be used to accurately analyze the remote sensing images.

2. The method for rapid analysis of massive remote sensing images according to claim 1, characterized in that: The method of quickly extracting the coordinate range and key attribute information of each scene data from the metadata file includes: obtaining the coordinates of the upper left corner and the lower right corner and the key attribute information of each scene data by parsing the metadata file. Assuming that the coordinates of the upper left corner are (x1, y1) and the coordinates of the lower right corner are (x2, y2), the coordinate range of the image data is expressed as: Among them, (x, y) represents the coordinates of a pixel point in the image data; The key attribute information includes the number of bands, horizontal resolution, vertical resolution and sampling rate.

3. The method for rapid analysis of massive remote sensing images according to claim 1, characterized in that: The calculation of the downsampling resolution of the remote sensing image thumbnail includes: calculating the horizontal and vertical ground resolution of the thumbnail, that is, the downsampling resolution, according to the geographic coordinates of the four corners of the original remote sensing image and the size of the thumbnail. The calculation formula is as follows: Assume that the geographic coordinates of the four corners of the original remote sensing image are the upper left corner (X UpperLeft , Y UpperLeft ), upper right corner (X UpperRight , Y UpperRight ), lower left corner (X BottomLeft , Y BottomLeft ), lower right corner (X BottomRight , Y BottomRight ); the width and height of the thumbnail are W bro and H bro , then the horizontal and vertical ground resolution R after thumbnail downsampling x_bro and R y_bro The calculation is as follows:

4. The method for rapid analysis of massive remote sensing images according to claim 3, characterized in that: The calculating of the image coordinate range of the remote sensing image thumbnail comprises: calculating the image coordinate range of the thumbnail according to the coordinate range of the original remote sensing image, the size of the thumbnail and the downsampling resolution, and the calculation process is: Assume that the pixel coordinates of the four corners of the thumbnail are the upper left corner (x UpperLeft ,y UpperLeft ), upper right corner (x UpperRight ,y UpperRight ), lower left corner (x BottomLeft ,y BottomLeft ), lower right corner (x BottomRight ,y BottomRight ), then: (x UpperLeft ,and UpperLeft )=(0,0) (x UpperRight ,y UpperRight )=(W bro ,0) (x BottomLeft ,y BpttpmLeft )=(0,H bro ) (x BottomRight ,y BottomRight )=(W bro ,H bro ) Assume that the geographic coordinates of the four corners of the thumbnail are the upper left corner (XB UpperLeft , Y.B. UpperLeft ), upper right corner (XB UpperRight , Y.B. UpperRight ), lower left corner (XB BottomLeft , Y.B. BottomLeft ), lower right corner (XB BottomRight , Y.B. BottomRight ), the image coordinate range of the thumbnail is: (XB UpperLeft ,YB UpperLeft )=(X UpperLeft ,Y UpperLeft ) (XB BottomLeft ,YB BottomLeft )=(X UpperLeft ,Y UpperLreft -R y_bro ×H bro ) 5. The method for rapid analysis of massive remote sensing images according to claim 4, characterized in that: Calculating the affine transformation coefficients of the thumbnail includes: First, assume that a point (x, y) in the thumbnail image is mapped to the geographic coordinate system (X, Y) after affine transformation. The affine transformation relationship is expressed as: Among them, the matrix represents the linear transformation part, Represents the translation part, xˊ is the geographic horizontal coordinate corresponding to the pixel, yˊ is the geographic vertical coordinate corresponding to the pixel, x is the pixel horizontal coordinate, y is the pixel vertical coordinate, a is the pixel resolution in the X direction, c and b are the scaling and rotation coefficients, d is the pixel resolution in the Y direction, e is the X coordinate of the pixel center in the upper left corner of the raster map, and f is the Y coordinate of the pixel center in the upper left corner of the raster map; Then substitute the pixel coordinates and geographic coordinates of the four corners of the thumbnail image into the affine transformation relationship calculation formula to obtain the affine transformation coefficient. The affine transformation relationship calculation formula can be used to calculate the geographic coordinate information of any point in the thumbnail.

6. The method for rapid analysis of massive remote sensing images according to claim 5, characterized in that: Substituting the four corner pixel coordinates and geographic coordinates of the thumbnail image into the affine transformation relationship calculation formula to calculate the affine transformation coefficients includes: The pixel coordinates of the upper left corner of the thumbnail (x UpperLeft ,y UpperLefu ) that is (0, 0) and the corresponding geographic coordinates (XB UpperLeft , Y.B. UpperLeft ) That is (X UpperLeft , Y UpperLeft ) into the affine transformation relationship calculation formula, we get: and=X UppetLeft f=Y UpperLeft The pixel coordinates of the upper right corner of the thumbnail (x UpperRight ,y UpperRight ) That is (x UpperRight ,0) and the corresponding geographic coordinates (XB UpperRight , Y.B. UpperRight ) into the affine transformation relationship calculation formula, we get: Set the pixel coordinates of the lower left corner of the thumbnail (x BottomLeft ,y BottomLeft ) that is (0, y BottomLeft ) and the corresponding geographic coordinates (XB BottomLeft , Y.B. BottomLeft ) into the affine transformation relationship calculation formula, we get:

7. The method for rapid analysis of massive remote sensing images according to claim 1, characterized in that: The geographic information system adopts GlobalMapper, ArcGIS, QGIS or MapInfo.

8. A system for rapid analysis of massive remote sensing images, characterized in that: Used to implement the method for rapid analysis of massive remote sensing images as described in any one of claims 1 to 7, the system comprises: Vector range file generation module, which is used to batch read metadata files of remote sensing image data, quickly extract the coordinate range and key attribute information of each scene data from the metadata file, and generate vector range files of massive remote sensing images; The thumbnail coordinate information generation module is used to calculate the downsampling resolution and image coordinate range of the remote sensing image thumbnail, calculate the affine transformation coefficient of the thumbnail according to the obtained downsampling resolution and image coordinate range of the thumbnail, and assign coordinate information to the thumbnail; The analysis module is used to quickly browse massive remote sensing images in the geographic information system by opening vector range files and overlaying thumbnails, and to accurately analyze remote sensing images using key attribute information of vector range files.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. 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 7 are implemented.

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