Method and system for large-scale and rapid extraction of urban white membrane

Abstract

The application provides a kind of urban white film wide range fast extraction method and system, belong to satellite remote sensing image processing and information extraction technical field.The method is by using resource three satellite stereo pair as data source, not only can reduce cost, expand monitoring range, also can be favorable to promote the development of domestic satellite application.In addition, the application proposes a kind of based on digital surface model (DSM) data and building two-dimensional contour data fusion ground elevation estimation method, combined with probability density estimation and quantile truncated mean calculation, accurately extract building roof elevation data and building ground elevation data, and calculate the difference value, obtain the height data of all buildings.The method does not need the process of separating terrain height and building height in conventional method, avoids the complicated steps such as point cloud editing, filtering, hollow interpolation, simple operation, reduces the operation cost, also greatly improves the efficiency of building height extraction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite remote sensing image processing and information extraction, and particularly relates to a large-scale urban white membrane rapid extraction method and system. BACKGROUND

[0002] Urban white membrane is a basic modeling method in the field of three-dimensional modeling, which mainly refers to a three-dimensional model containing only the shape and height information of buildings, without texture and detail information. Such model has wide application value in the fields of urban planning, public management, disaster prevention and mitigation, navigation and smart city construction, and has important significance for sustainable development, efficient use of urban resources and provision of public services for residents.

[0003] The production of urban white membrane is a complex process involving data collection, processing and three-dimensional modeling, and its core elements include building contour data and building height data. Among them, the two-dimensional contour data of buildings can be obtained in various ways and is relatively easy to obtain, while the height data of buildings is more difficult to obtain. How to quickly and accurately obtain building height information has become a hot research topic today.

[0004] Traditional building height measurement methods, such as outdoor measurement by surveying and mapping personnel, laser radar method, aerial stereo image method, etc., have the advantages of mature technology and high precision, but have the disadvantages of high cost, complex processing process, poor timeliness, etc. With the development of remote sensing technology, high-resolution satellite image stereo mapping technology has been rapidly developed and widely applied in building height extraction. Compared with artificial outdoor measurement, aerial photography and laser radar point cloud data, satellite remote sensing has the advantages of large monitoring area, wide coverage, low data cost, rapid acquisition and continuous monitoring and updating, and has gradually become an important data source for building height extraction.

[0005] At present, the methods for extracting building height based on high-resolution satellite images mainly include two categories: shadow method and stereo image pair method.

[0006] The height estimation method based on shadow can be based on a single remote sensing image, and the building height can be estimated according to the length of the building shadow in the high-resolution optical image and the geometric relationship between the sun and the sensor. Common algorithms include edge detection method, threshold segmentation method, etc. This kind of method is suitable for the case that the building is high, the gap between adjacent buildings is large, and there is no obstruction around the building. When the shadow of the building is blocked or confused with other objects, the estimation accuracy is poor.

[0007] The stereoscopic reconstruction method based on satellite stereopairs uses the three-dimensional geographic information contained in the satellite stereopairs to estimate the height of buildings. The method is based on the principle of binocular stereovision, adopts kernel line resampling stereomatching and dense point cloud reconstruction to generate DSM (Digital Surface Model) data, which contains both terrain height and ground object height. By further separating the ground object height, the height of the building can be obtained. This method is not affected by the environment around the building and has stronger universality in the case of dense distribution of buildings with uniform height.

[0008] Currently, the method for making city white film based on satellite stereopairs mainly uses sub-meter high-resolution stereopair images. First, deep learning technology or object-oriented classification machine learning method is used to extract buildings, and then artificial correction is combined to obtain two-dimensional contour data of buildings. Then, based on the principle of stereovision, kernel line resampling stereomatching and dense point cloud reconstruction are used to calculate and generate kernel line to image to obtain DSM data, and point cloud data filtering algorithm is used to obtain DEM (Digital Elevation Model) data. Then, the DSM data and DEM data are overlaid and analyzed to calculate the difference to obtain the ground object height layer. Finally, the two-dimensional contour data of buildings and the ground object height layer are spatially overlaid to obtain the height information of each building plot, and finally the three-dimensional model of city buildings is realized. The core of this technical method is to separate the terrain height and the ground object height by using the point cloud data filtering algorithm to obtain the height of the building. The separation process involves point cloud data classification, point cloud data editing and filtering, hollow interpolation filling, and image base map assistance.

[0009] It should be noted that: DSM contains the elevation information of the top of all objects on the ground surface (such as buildings, vegetation, roads, etc.). DSM not only records the ups and downs of the terrain itself (such as hills and valleys), but also contains the height information of the ground objects. DEM only represents the elevation information of the exposed terrain on the earth's surface (such as mountains and plains), and does not contain the height of buildings, vegetation, etc. (the elevation of the exposed terrain on the earth's surface needs to be generated by subtracting the height of the ground object from the DSM).

[0010] At present, there are mainly two defects in the following two schemes for making city white film using sub-meter high-resolution satellite stereopairs:

[0011] First, the satellite data source, usually 0.3m-0.5m resolution satellite stereo image (such as Pleiades, Geoeye, worldview-2 and worldview-3, etc.) and 0.7m resolution GF-7 satellite stereo image, the advantages of such data source is that the higher the resolution, the higher the accuracy of building contour extraction and building height information extraction, which can meet the 1:2000 to 1:10000 scale surveying and mapping requirements, but the obvious disadvantage is that the price of this kind of data is very expensive, the cost is very high, and the width of the data is small, the single scene width is less than 20 kilometers, and the monitoring range is small;

[0012] Second, the extraction of building height information is also the most critical part, which is usually first using stereo image data to generate DSM data, then separating the terrain height from the object height by point cloud filtering algorithm to obtain DEM data, and finally using overlay analysis to obtain the height information of the building. The core process involves point cloud data classification, point cloud data editing and filtering, hollow interpolation filling and image base map auxiliary steps. Although the height information of the building can be accurately extracted, the process involves manual editing, which requires high professional ability of the technical personnel, low editing efficiency and low automation degree. In addition, the filtering of point cloud data and the interpolation filling of hollow will further increase the operation cost. SUMMARY

[0013] The purpose of the present application is to overcome the above technical deficiencies, provide a kind of city white film wide range fast extraction method and system, to solve the related technical problems in the generation process of city white film, the extraction of building height information involves manual editing, which requires high professional ability of the technical personnel, low editing efficiency and low automation degree.

[0014] To achieve the above technical purpose, the present application adopts the following technical scheme:

[0015] According to the first aspect of the present application, a kind of city white film wide range fast extraction method is provided, comprising:

[0016] Step S11, according to the range of working area, satellite stereo image data of resource three satellite stereo image covering working area is collected, the satellite image data at least includes front view image data and rear view image data;

[0017] Step S12, the rear view image data is preprocessed to obtain satellite rear view image base map;

[0018] Step S13, the rear view image base map is extracted building graph spot vector by using object-oriented classification machine learning method, and combined with artificial visual correction adjustment, to obtain building two-dimensional contour data;

[0019] Step S14, using a preset satellite processing module, importing the forward-looking image data and the rear-looking image data, and extracting digital surface model (DSM) data;

[0020] Step S15, according to the building two-dimensional contour data, using a quantile truncation method, extracting building roof elevation data from the DSM data;

[0021] Step S16, according to the building two-dimensional contour data, combining a probability density estimation method and a quantile truncation mean method, extracting building ground elevation data from the DSM data;

[0022] Step S17, performing a difference operation on the building roof elevation data and the building ground elevation data to obtain height data of all buildings;

[0023] Step S18, generating a city white film based on the building two-dimensional contour data and the height data of all buildings.

[0024] Preferably, the step S12 comprises:

[0025] Step S121, radiation correction processing: using a radiation calibration module and an atmospheric correction module in a remote sensing image processing and analysis software ENVI, sequentially performing radiation calibration and atmospheric correction on the rear-looking image data of the ZY-3 satellite stereo image pair to eliminate the influence of atmospheric scattering and absorption on radiation;

[0026] Step S122, orthographic correction processing: using an orthographic correction module in the remote sensing image processing and analysis software ENVI, performing orthographic correction on the rear-looking image map that has undergone the radiation correction processing of step S121 to eliminate the geometric distortion caused by terrain undulations, and obtaining a satellite rear-looking image base map.

[0027] Preferably, the step S13 comprises:

[0028] Step S131, using a supervised classification module in the remote sensing image processing and analysis software ENVI, selecting an appropriate amount of building samples, and using an object-oriented classification method to classify and extract building polygons to obtain building polygon vectors on the satellite rear-looking image base map.

[0029] Step S132, using an artificial visual correction method in a geographic information system software ArcGIS Pro software, performing artificial editing and correction on the building polygon vectors to obtain building two-dimensional contour data.

[0030] Preferably, the step S14 comprises:

[0031] Step S141, satellite stereo data import: using the SAT Master 10.0 satellite processing module in the Trimble Inpho Photogrammetry 10.0 software for remote sensing data processing, through at least including creating a project, importing the front-view image data and the rear-view image data, importing RPC information, creating a scene, and a project inspection step, the import of satellite stereo image pair data is completed;

[0032] Step S142, project image preprocessing: opening the project created in step S141, at least including image pyramid creation and image radiation correction processing operations are performed to complete the project processing setting;

[0033] Step S143, aerial triangulation processing: using the MATCH-AT satellite triangulation module in the Trimble Inpho Photogrammetry 10.0 software, automatically identifying the same name image points in multiple satellite stereo image pairs, and extracting these points as the basic control data for aerial triangulation; and using the results of aerial triangulation, iteratively optimizing the strict geometric model parameters RPCs of the satellite image to improve the geographical positioning accuracy of the image;

[0034] Step S144, generating DSM data: using the Match-T DSM module in the Trimble Inpho Photogrammetry 10.0 software, using the corrected RPCs, combining the same name image points matched by multi-view images, to generate DSM data containing building roof height.

[0035] Preferably, the step S15 comprises:

[0036] Step S151, according to the building two-dimensional contour data, based on the geographic information system software ArcGIS Pro software, obtaining the elevation value of each building area from the DSM data pixel by pixel;

[0037] Step S152, using the quantile truncation method, sorting the DSM elevation data in the building planar region from small to large, and selecting the elevation value at the 98% quantile as the roof elevation to effectively remove the noise and abnormal elevation values that may exist in the DSM data.

[0038] Preferably, the step S151 is specifically:

[0039] If the building planar region contains pixels, and the corresponding elevation value is , the elevation value extraction formula is: wherein, 1≤i≤n, is the coordinate of each pixel in the DSM.

[0040] The step S152 is specifically:

[0041] The sorted elevation value is denoted as , and the score is defined as: , wherein p represents the quantile ratio, and the value range is ;

[0042] The elevation value of the 98% quantile is denoted as , and the roof elevation is denoted as: .

[0043] Preferably, the step S16 comprises:

[0044] The step S161 comprises: according to the two-dimensional contour data of the building, expanding each building plot by creating a 20-meter buffer zone, extracting the DSM elevation data of the building plot and its surrounding area in the range, and ensuring complete coverage of the terrain changes around the building.

[0045] The step S162 comprises: analyzing the DSM data extracted in the buffer zone by a probability density estimation method, constructing a distribution model of the elevation data, and optimizing the data by a quantile truncated mean method.

[0046] Preferably, the step S161 is specifically:

[0047] If the buffer zone of the building plot is , and the buffer radius is d, the geometric expression is:

[0048] ; wherein (x, y) respectively represent the horizontal coordinate and vertical coordinate values of any pixel in the buffer zone in the image coordinate system, and (x r , y r ) represent the horizontal coordinate and vertical coordinate values of the center pixel of the building plot in the image coordinate system.

[0049] The elevation data set extracted in the buffer zone range is:

[0050] ; wherein z i represents the pixel point with the image coordinates (x i , y i ) in the buffer zone , and the corresponding elevation value in the DSM data.

[0051] ​The step S162 adopts the quantile truncated mean method to optimize the data, including: arranging all the extracted elevation values in ascending order, selecting the elevation values in the 5%-10% interval to calculate the mean value, and taking the mean value as the building ground elevation, specifically:

[0052] The elevation values extracted in the buffer area are sorted in ascending order and recorded as ; the elevation values in the 5%-10% interval are selected to calculate the mean value as the ground elevation value:

[0053] , wherein - +1;

[0054] The probability density estimation is used to construct a distribution model of the elevation data: ;

[0055] , wherein is a kernel function, and the commonly used Gaussian kernel is: ,

[0056] is a bandwidth, which determines the smoothness of the distribution, and is determined according to historical experience values or experimental data.

[0057] Preferably, the step S17 specifically includes:

[0058] In the geographic information system software ArcGIS Pro software, the difference between the value corresponding to the building roof elevation field and the value corresponding to the building ground elevation field in the attribute table of the building plot vector data is calculated to obtain the height data of the building plot.

[0059] According to the second aspect of the present application, a system for large-scale and rapid extraction of urban white film is provided, comprising:

[0060] A collection module is configured to collect resource satellite stereo image data of ZY-3 satellite covering a working area according to the range of the working area, wherein the satellite image data at least includes forward-looking image data and rear-looking image data.

[0061] A processing module is configured to pre-process the rear-looking image data to obtain a satellite rear-looking image base map.

[0062] The processing module is further configured to extract building plot vectors from the rear-looking image base map by using an object-oriented classification machine learning method, and obtain two-dimensional contour data of the buildings by combining artificial visual correction and adjustment.

[0063] The extraction module is configured to import the front-view image data and the rear-view image data by using a preset satellite processing module, and extract digital surface model (DSM) data;

[0064] The method is also configured to extract building roof elevation data from the DSM data by using a quantile-truncated method according to the two-dimensional building contour data;

[0065] The method is also configured to extract building ground elevation data from the DSM data by using a method combining probability density estimation and quantile-truncated mean according to the two-dimensional building contour data;

[0066] The calculation module is configured to perform difference operation on the building roof elevation data and the building ground elevation data to obtain height data of all buildings.

[0067] The generation module is configured to generate a city white film based on the two-dimensional building contour data and the height data of all buildings.

[0068] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0069] By using the ZY-3 satellite stereo pair as a data source, which is the first domestic high-resolution stereo mapping satellite, the resolution is 2.1 m, the width is 52 km, and the method has the advantages of low cost, large data acquisition, wide coverage, etc., and can meet the requirements of 1:50,000 scale mapping, and fully meet the requirements of city white film level three-dimensional modeling; by using the data, the cost can be reduced, the monitoring range can be expanded, and the development of domestic satellite application can be promoted.

[0070] In addition, the present application also provides a ground feature elevation estimation method based on the fusion of digital surface model (DSM) data and two-dimensional building contour data, which combines probability density estimation and quantile-truncated mean calculation to accurately extract building roof elevation data and building ground elevation data, and calculates the difference between the two to obtain height data of all buildings. The method does not need to separate the terrain height and the building height in the conventional method, avoids the complicated steps such as point cloud editing, filtering and hole interpolation, is simple to operate, reduces the operation cost, greatly improves the efficiency of building height extraction, has high automation level, and can further realize the automatic production of city white film. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 is a flow chart of a city white film large-scale rapid extraction method according to an example embodiment;

[0072] Figure 2 is a schematic block diagram of a city white film large-scale rapid extraction system according to an example embodiment;

[0073] Figure 3 is a city white film generation effect diagram shown according to an exemplary embodiment;

[0074] Figure 4 is a flow chart of a city white film large-scale rapid extraction method shown according to another exemplary embodiment. DETAILED DESCRIPTION

[0075] In order to enable persons skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should belong to the protection scope of the present application.

[0076] Embodiment one

[0077] Figure 1 is a flow chart of a city white film large-scale rapid extraction method shown according to an exemplary embodiment, referring to Figure 1 , the method comprises:

[0078] Step S11, according to the working area range, satellite stereo image data of ZY-3 satellite covering the working area is collected, and the satellite image data at least contains forward-looking image data and rear-looking image data;

[0079] Step S12, the rear-looking image data is preprocessed to obtain a satellite rear-looking image base map;

[0080] Step S13, the rear-looking image base map is used to extract building polygon vector by using an object-oriented classification machine learning method, and combined with artificial visual correction adjustment, building two-dimensional contour data is obtained;

[0081] Step S14, the forward-looking image data and the rear-looking image data are imported by using a preset satellite processing module, and digital surface model (DSM) data is extracted;

[0082] Step S15, according to the building two-dimensional contour data, a quantile truncation method is used to extract building roof elevation data from the DSM data;

[0083] Step S16, according to the building two-dimensional contour data, combined with the method of probability density estimation and quantile truncation mean, building ground elevation data is extracted from the DSM data;

[0084] Step S17, difference value operation is performed on the building roof elevation data and the building ground elevation data to obtain height data of all buildings;

[0085] Step S18, generating a city white film based on the building two-dimensional contour data and the height data of all buildings.

[0086] It should be noted that the technical scheme provided in the embodiment is loaded in an electronic device for running in specific practice. The electronic device includes at least one processor, and a memory in communication connection with the at least one processor. The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the embodiment. The electronic device includes but is not limited to a client computer, a local server, and / or a cloud server.

[0087] It should be noted that the ZY-3 satellite is equipped with three cameras, i.e., a forward-looking camera, a nadir-looking camera, and an aft-looking camera, to form multi-angle stereo observation capability.

[0088] Nadir image: vertically downward shooting, used for basic geographic information extraction (such as orthophoto).

[0089] Forward-looking / aft-looking image: oblique shooting, used for constructing stereo pairs, and generating high-precision DSM (digital surface model) and terrain elevation data through multi-view image matching.

[0090] In the ZY-3 satellite stereo pairs, the forward-looking image data and the aft-looking image data are image data of different angles obtained based on satellite multi-angle imaging technology, and the specific meanings are as follows:

[0091] 1. Forward-looking image data

[0092] Definition: The forward-looking image is an image obliquely shot from the side front of the satellite in the forward flight direction (i.e., the satellite orbit advancing direction), which is usually related to the tilt angle of the forward-looking camera (such as about 22°).

[0093] Action: Provides oblique angle information of the ground object in the satellite advancing direction, which, in combination with the forward-looking image and the nadir image (vertically downward), can form the geometric relationship of the stereo pairs, and is used to calculate the elevation information of the ground point.

[0094] 2. Aft-looking image data

[0095] Definition: The aft-looking image is an image obliquely shot from the side rear of the satellite in the backward flight direction (i.e., the satellite orbit retreating direction), which is usually related to the tilt angle of the aft-looking camera (such as about 22°).

[0096] Action: Provides oblique angle information of the ground object in the satellite retreating direction, which, in combination with the aft-looking image and the nadir image, further supplements the geometric constraints of the stereo pairs, and enhances the accuracy and reliability of three-dimensional reconstruction.

[0097] It can be understood that the technical scheme provided by the embodiment adopts the resource three satellite stereo pair as a data source, which is the first civil high-resolution stereo mapping satellite independently designed and launched by China, has a resolution of 2.1 m, a width of 52 km, has advantages of low acquisition cost, large data acquisition, wide coverage, etc., can meet the 1:50,000 scale mapping requirements, and fully meets the city white film level three-dimensional modeling; using the data, not only can reduce the cost, expand the monitoring range, but also can promote the development of domestic satellite application.

[0098] In addition, the technical scheme provided by the embodiment also proposes a ground feature elevation estimation method based on fusion of digital surface model (DSM) data and building two-dimensional contour data, which combines probability density estimation and quantile truncated mean calculation to accurately extract building roof elevation data and building ground elevation data, and calculate the difference between the two to obtain the height data of all buildings. This method does not need to separate the terrain height and building height as in the conventional method, avoiding the complicated steps such as point cloud editing, filtering, and hole interpolation, and is simple to operate, reduces the operation cost, and greatly improves the efficiency of building height extraction. This method has a high level of automation and can further realize the automatic production of city white film.

[0099] In specific practice, in step S12, according to the past data processing experience, the DSM data produced by the resource three satellite stereo pair is matched with the rear view image of the stereo pair, so in specific practice, only the rear view image data is preprocessed, including radiation correction, orthographic correction and other steps, to obtain a satellite rear view image base map as a base map for subsequent building two-dimensional contour data extraction.

[0100] Therefore, referring to Figure 4 , the step S12 includes:

[0101] Step S121, radiation correction processing: using the radiation calibration module and atmospheric correction module in the remote sensing image processing and analysis software ENVI, the rear view image data of the resource three satellite stereo pair is sequentially subjected to radiation calibration and atmospheric correction to eliminate the influence of atmospheric scattering and absorption on radiation;

[0102] Step S122, orthographic correction processing: using the orthographic correction module in the remote sensing image processing and analysis software ENVI, the rear view image processed by the radiation correction processing in step S121 is subjected to orthographic correction to eliminate the geometric distortion caused by the terrain undulation, to obtain a satellite rear view image base map.

[0103] It is worth noting that ENVI (The Environment for Visualizing Images) is a professional remote sensing image processing and analysis software widely used in geographic information systems (GIS), environmental monitoring, agriculture, forestry, urban planning, disaster assessment and other fields. It provides a complete set of tools for extracting information from satellite, aerial or unmanned aerial vehicle images and conducting analysis.

[0104] ENVI supports reading, correction, fusion and mosaicking of various sensor data such as Landsat, Sentinel, MODIS, ZY-3 optical satellite and SAR radar data. It provides pre-processing functions such as radiometric calibration, atmospheric correction and geometric correction to ensure data quality.

[0105] In specific practice, referring to Figure 4 , the step S13 comprises:

[0106] Step S131, on the satellite rear view image base map, using the supervised classification module in the remote sensing image processing and analysis software ENVI, selecting appropriate building samples, using the object-oriented classification method to classify and extract the building polygon, obtaining the building polygon vector;

[0107] Step S132, in combination with the satellite rear view image base map, in the geographic information system software ArcGIS Pro software, using the artificial visual correction method, editing and correcting the building polygon vector to obtain the building two-dimensional contour data (i.e. building polygon vector data).

[0108] It is worth noting that ArcGIS Pro is a new generation of professional geographic information system (GIS) software developed by Esri, integrating map making, spatial data analysis, three-dimensional visualization, data management and other functions, widely used in urban planning, natural resource management, environmental protection, emergency response, transportation and logistics fields. It is the core desktop tool of ArcGIS ecosystem, supporting the whole process from simple map drawing to complex spatial modeling.

[0109] In specific practice, referring to Figure 4 , the step S14 comprises:

[0110] Step S141, satellite stereo data import: using the SAT Master 10.0 satellite processing module in the photogrammetry and remote sensing data processing software Trimble InphoPhotogrammetry 10.0, through at least including creating project, importing the front view image data and rear view image data, importing RPC information, creating scene, project checking steps, completing the import of satellite stereo image pair data;

[0111] Step S142, project image preprocessing: open the project created in step S141, and perform at least image pyramid creation and image radiation correction processing operations to complete project processing settings;

[0112] Step S143, aerial triangulation processing: using the MATCH-AT satellite triangulation module in Trimble Inpho Photogrammetry 10.0 software, automatically identify the same points in multiple satellite stereo pairs, and extract these points as the basis for control data for aerial triangulation; and using the results of aerial triangulation, iteratively optimize the strict geometric model parameters RPCs of the satellite images to improve the geographical positioning accuracy of the images;

[0113] Step S144, generating DSM data: using the Match-T DSM module in Trimble Inpho Photogrammetry 10.0 software, using the corrected RPCs, combining the same points matched by multiple-view images, to generate DSM data containing building roof height.

[0114] It should be noted that Trimble Inpho Photogrammetry 10.0 is a professional photogrammetry and remote sensing data processing software developed by Trimble Company, focusing on extracting high-precision geographic information from aerial, satellite or unmanned aerial vehicle images, generating terrain models, orthophotos (DOM), three-dimensional maps and other results. It is widely used in surveying and mapping, engineering planning, natural resource management, disaster assessment and other fields, and is especially suitable for processing large-scale high-resolution image data. The software generates high-resolution DSM (containing the height of all objects on the ground) and DEM (bare terrain elevation) data based on image matching results. The software supports filtering classification to separate ground points and non-ground points (such as buildings and vegetation).

[0115] In step S142, completing project processing settings (such as image pyramid creation, radiation correction, etc.) is the key basis for efficient operation of subsequent processes and generation of high-precision DSM data.

[0116] 1. The role of image pyramid creation

[0117] Accelerate image processing: image pyramid stores images in multiple resolutions (such as from low resolution to high resolution), which can quickly call low-resolution images for rough calculation in subsequent processing (such as matching and adjustment), reducing the amount of calculation and improving efficiency.

[0118] Support large-scale data management: For massive satellite images (such as ZY-3 multi-scene stitching data), pyramid structure can effectively manage memory and storage resources, avoiding performance bottlenecks when directly processing raw high-resolution images.

[0119] Adapt to multi-scale analysis: In aerial triangulation (step S143) and DSM generation (step S144), different stages may require different resolution image data (such as low resolution for coarse matching and high resolution for fine matching), and pyramid structure can be flexibly supported.

[0120] 2. Role of image radiation correction

[0121] Eliminate sensor noise and distortion: Satellite sensors may introduce noise or radiation distortion (such as dark corner effect) during imaging due to factors such as light, atmospheric scattering, etc. Radiation correction ensures the radiation consistency between multi-scene images by adjusting image brightness and contrast, avoiding matching errors.

[0122] Improve image matching accuracy: Without radiation correction, it may be difficult to match the same point between different images due to terrain shadows, cloud coverage, or sensor response differences. After correction, the texture features of the image are clearer, enhancing the robustness of the matching algorithm (such as feature point extraction).

[0123] Support multi-temporal data fusion: In scenarios such as disaster monitoring that require comparison of different temporal images, radiation correction can eliminate the radiation changes caused by time differences, improving the reliability of change detection.

[0124] Aerial triangulation in step S143: The MATCH-AT module relies on the pre-processed image pyramid to quickly extract connection points and optimize RPCs parameters. If a pyramid is not created, directly processing raw images may result in excessive computation time or even memory overflow.

[0125] DSM generation in step S144: The Match-T DSM module requires radiation-consistent images as input, otherwise the generated DSM may have noise such as stripes and spots, affecting terrain detail extraction.

[0126] RPCs are rational polynomial coefficients provided by satellite manufacturers, used to convert pixel / row and column coordinates to ground coordinates (latitude, longitude, and elevation).

[0127] The error of RPCs is calculated by the control points (connection points and adjusted exterior orientation elements) obtained through aerial triangulation, and the model parameters are corrected.

[0128] The process of step S143 is: automatically extract connection points → generate initial matching point data → optimize RPCs → improve global geometric accuracy.

[0129] Effect: Eliminate systematic errors of original RPCs (such as sensor distortion, positioning deviation caused by terrain undulation). If only relying on original RPCs, it may produce larger positioning error in mountainous or urban high-rise areas. By connecting point optimization RPCs, it can significantly reduce error transmission and ensure that the generated DSM / DEM data is more true and reliable. Step S143 can improve the accuracy of subsequent DSM / DEM generation, especially in complex terrain or large area.

[0130] The flow from step S143 to S144 is essentially to apply the geometric correction results of aerial triangulation to DSM generation. The core steps include: exporting aerial triangulation results (RPCs, exterior orientation elements); import into the DSM module and configure parameters; perform dense matching to generate DSM. This process embodies the full-process automation capability of Trimble Inpho software. Users only need to ensure the quality of aerial triangulation results to efficiently generate high-precision DSM.

[0131] In specific practice, referring to Figure 4 , the step S15 comprises:

[0132] Step S151, according to the building two-dimensional contour data, based on geographic information system software ArcGIS Pro software, obtaining the elevation value of each building area from the DSM data pixel by pixel;

[0133] Step S152, using the quantile truncation method, sorting the DSM elevation data in the building graph spot area from small to large, and selecting the elevation value at the 98% quantile as the roof elevation, so as to effectively remove the noise and abnormal elevation value in the DSM data.

[0134] In specific practice, the step S151 is specifically:

[0135] If the building graph spot contains pixels, the corresponding elevation value is , and the elevation value extraction formula is: , where 1≤i≤n, is the coordinate of each pixel in DSM;

[0136] The step S152 is specifically:

[0137] Let the sorted elevation value be , and define the score as: , where p represents the quantile ratio, and the value range ;

[0138] Taking the elevation value of 98% quantile as the roof elevation: .

[0139] In the specific practice, referring to Figure 4 , the step S16 comprises:

[0140] The step S161, according to the building two-dimensional contour data, the building planar patch is expanded by creating a 20-meter buffer zone (expand the analysis range, avoid the influence of the building edge on the ground elevation estimation), in the range, the DSM elevation data of the building planar patch and its surrounding area is extracted, and the topographic change around the building is ensured to be completely covered;

[0141] The step S162, the DSM data extracted in the buffer zone is analyzed by a probability density estimation method, a distribution model of the elevation data is constructed, and a quantile truncated mean method is used to optimize the data.

[0142] In the specific practice, the step S161 is specifically:

[0143] If the buffer zone of the building planar patch is , the buffer radius is d, and the geometric expression is:

[0144] ; wherein, (x, y) respectively represent the horizontal coordinate and vertical coordinate values of any pixel in the buffer zone in the image coordinate system, (x r , y r ) represents the horizontal coordinate and vertical coordinate values of the center pixel of the building planar patch in the image coordinate system;

[0145] The elevation data set is extracted in the buffer zone range:

[0146] ; wherein, z i represents the pixel point with the image coordinates (x i , y i ) in the buffer zone , and the elevation value corresponding to the DSM data is obtained;

[0147] The step S162 uses the quantile truncated mean method to optimize the data, which comprises: arranging all the extracted elevation values in ascending order, selecting the elevation values in the 5%-10% interval for mean calculation, and taking the mean value as the building ground elevation, specifically:

[0148] The extracted elevation values in the buffer zone are arranged in ascending order, denoted as ; the elevation values in the 5%-10% interval are selected , and the mean value is calculated as the ground elevation value:

[0149] wherein, - +1;

[0150] with probability density estimation Constructing the distribution model of the elevation data: ;

[0151] wherein, is a kernel function, and a commonly used Gaussian kernel: ,

[0152] is a bandwidth, which determines the smoothness of the distribution, and is determined according to historical experience values or experimental data.

[0153] In specific practice, the step S17 is specifically:

[0154] In the geographic information system software ArcGIS Pro software, the numerical value corresponding to the building roof elevation field and the numerical value corresponding to the building ground elevation field in the building plan vector data attribute table are calculated by difference, to obtain the height data of the building plan.

[0155] In summary, the technical scheme provided by the embodiment addresses the problems of high cost and small monitoring range caused by the high price and narrow width of sub-meter stereo image pair data sources in the production process of the prior art city building white film, and the problems of high operation cost, low production efficiency, low automation level, high professional ability requirement, and many other problems caused by the complicated steps of separating the terrain height and the feature height in the building height information extraction process. A city white film rapid extraction method based on ZY-3 satellite stereo images is proposed, which can reduce the cost, improve the efficiency, is simple to operate, has high precision and automation level, and can realize large-scale automatic and rapid production of city white film.

[0156] Embodiment Two

[0157] Figure 2 is a schematic block diagram of a city white film large-scale rapid extraction system 100 according to an exemplary embodiment, referring to Figure 2 The system 100 comprises:

[0158] The acquisition module 101 is configured to acquire ZY-3 satellite stereo image data covering the working area according to the working area range, wherein the satellite image data at least comprises front-view image data and rear-view image data.

[0159] The processing module 102 is configured to pre-process the rear-view image data to obtain a satellite rear-view image base map.

[0160] The building two-dimensional contour data is used for extracting building roof elevation data from the DSM data by using a quantile truncation method.

[0161] The extraction module 103 is configured to import the front-view image data and the rear-view image data by using a preset satellite processing module, and extract digital surface model (DSM) data.

[0162] The building two-dimensional contour data is used for extracting building roof elevation data from the DSM data by using a quantile truncation method.

[0163] The building two-dimensional contour data is used for extracting building ground elevation data from the DSM data by using a method combining probability density estimation and quantile truncation mean value.

[0164] The calculation module 104 is configured to perform difference operation on the building roof elevation data and the building ground elevation data, and obtain height data of all buildings.

[0165] The generation module 105 is configured to generate city white film based on the building two-dimensional contour data and the height data of all buildings.

[0166] It should be noted that the technical solution provided in the embodiment is loaded in an electronic device for running in specific practice. The electronic device includes at least one processor, and a memory connected with the at least one processor in communication. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the embodiment. The electronic device includes but is not limited to a client computer, a local server, and a cloud server.

[0167] The implementation manners of the modules are described in the implementation manners of the corresponding steps in Embodiment 1, and the embodiment will not be described here.

[0168] It can be understood that the technical solution provided in the embodiment uses the ZY-3 satellite stereo image pair as a data source. It is the first civil high-resolution stereo mapping satellite independently designed and launched by China, with a resolution of 2.1 m and a width of 52 km. It has the advantages of low cost, large data acquisition, wide coverage, and can meet the requirements of 1:50,000 scale surveying and mapping, and fully meet the requirements of city white film level three-dimensional modeling. Using this data can not only reduce the cost and expand the monitoring range, but also promote the development of domestic satellite applications.

[0169] In addition, the technical scheme provided by the embodiment provides a ground feature elevation estimation method based on fusion of digital surface model (DSM) data and two-dimensional building contour data, combines probability density estimation and quantile truncated mean calculation, accurately extracts building roof elevation data and building ground elevation data, and calculates the difference between the two to obtain height data of all buildings. The method does not need to separate the terrain height and the building height in the conventional method, avoids complex steps such as point cloud editing, filtering and hole interpolation, is simple to operate, reduces the operation cost, greatly improves the efficiency of building height extraction, has a high automation level, and can further realize automatic production of city white film.

[0170] In specific practice, in order to verify the feasibility of the city white film large-scale rapid extraction method and system provided by the application, the city white film production case of Jintai County in Gansu Province is used to illustrate the city white film rapid extraction method based on ZY-3 satellite stereo image pairs provided by the embodiment.

[0171] The working area is located in the county area of Jintai County in Gansu Province, and the buildings are densely distributed. According to the project production requirements, city white film meeting the 1:50000 scale is needed for three-dimensional simulation application. Considering the project cost, archive data and precision requirements, the method of the application is used to extract the building polygon vector and building height information of the working area by using ZY-3 satellite stereo image pair data, so as to establish the city white film of the working area.

[0172] Firstly, the back-looking image of ZY-3 stereo image pairs is sequentially subjected to radiation calibration, atmospheric correction, orthorectification and other pretreatments by using the radiation correction module and orthorectification module of ENVI software.

[0173] Secondly, the supervised classification module of ENVI software is used to adopt the object-oriented classification method, that is, support vector machine (SVM), to select appropriate building sample data, classify and extract the back-looking image map, and output the building polygon vector.

[0174] Then, the back-looking image and the building polygon vector extracted by ENVI are imported into ArcGIS Pro software, the effect of the two overlaid is checked by manual visual method, the building polygon missing extraction and mis-extraction are manually edited and corrected, so as to obtain accurate building polygon vector data.

[0175] Then, the satellite processing module SATMaster 10.0 in Trimble Inpho Photogrammetry 10.0 software is used to process the ZY-03 satellite stereo image pair, and the working area digital surface model (DSM) data is obtained.

[0176] Subsequently, the building height data extraction is carried out, mainly including building roof elevation data extraction and building ground elevation data extraction: (1) the quantile truncation method is used to extract the building roof elevation data, specifically: based on the DSM data and the building polygon vector data, all the DSM elevation values corresponding to the pixels in each building polygon range are arranged in ascending order, and the value at 98% is taken as the roof elevation value of the building, so as to obtain the building roof elevation data; (2) the method combining probability density estimation and quantile truncation mean is used to extract the building ground elevation data, specifically: first, the building polygon vector is expanded by 20m buffer, then all the DSM elevation values corresponding to the pixels in each building polygon range after expansion are arranged in ascending order, and the DSM average value of the pixels in the 5%-10% interval is taken as the ground elevation value of the building, and then the attribute connection of the building polygon vector and the buffer vector is used, so as to obtain the building ground elevation data.

[0177] Thirdly, in the ArcGIS Pro software, the attribute table of the building polygon vector data is calculated by raster, and the building roof elevation (Elevation field) is subtracted from the building ground elevation field (Ground_Ele field), so as to obtain the building height data (Height field), and the results are shown in Table 1:

[0178]

[0179] Table 1: Attribute structure table of building polygon vector data (part of the polygon)

[0180] The accuracy of the building height data extraction result in the application is verified by the measured building point height, and the verification result shows that the average absolute accuracy of the building height data extracted by the application is 2.93m, which meets the 1:50000 scale surveying and mapping requirement, and details are shown in Table 2.

[0181]

[0182] Table 2: Accuracy verification table of building height extraction result of the application

[0183] Finally, in the GIS software or three-dimensional simulation software, the building polygon vector data and the corresponding height data field are used to generate the city white film, and in this case, the city white film is generated in ArcGIS Pro, and the effect is shown in Figure 3 .

[0184] The results show that the city white film rapid extraction method based on the resource three satellite stereo image pair can produce city white films in a large range, low cost, fast and efficient, and has simple operation, high precision and automation level, can further realize large-scale automatic and rapid production of city-level building white films, and has high practical application value.

[0185] The above embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0186] The integrated units in the above embodiments, if realized in the form of software function units and sold or used as independent products, can be stored in the above computer-readable storage medium. Based on such understanding, the technical solutions of the present application or the whole or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing one or more computer devices (which can be personal computers, servers or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0187] In the above embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0188] In the several embodiments provided by the present application, it should be understood that the disclosed client can be implemented in other ways. Of course, the above device embodiment is only illustrative, for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual units can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0189] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the present embodiment scheme.

[0190] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing unit, or each of the units can exist physically, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0191] The above only describes the preferred embodiments of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for large-scale rapid extraction of urban white membranes, characterized in that, Comprise: Step S11, according to the working area range, the resource three satellite stereo image satellite image data covering the working area is collected, the satellite image data at least contains forward looking image data and rear view image data; Step S12, the rear view image data is pretreated to obtain satellite rear view image base map; Step S13, on the rear view image base map, the machine learning method of object-oriented classification is used to extract building graph spot vector, combined with artificial visual correction adjustment, obtain building two-dimensional contour data; Step S14, using the preset satellite processing module, import the forward looking image data and rear view image data, extract to obtain digital surface model (DSM) data; Step S15, according to the building two-dimensional contour data, using the quantile truncation method, the building roof elevation data is extracted from the DSM data; Step S16, according to the building two-dimensional contour data, combined with the method of probability density estimation and quantile truncated mean, the building ground elevation data is extracted from the DSM data; Step S17, the building roof elevation data and the building ground elevation data are operated by difference to obtain the height data of all buildings; Step S18, based on the building two-dimensional contour data and the height data of all buildings, the city white film is generated; Wherein, the step S16 comprises: Step S161, according to the building two-dimensional contour data, each building graph spot is expanded by creating a 20m buffer zone, and the DSM elevation data of the building graph spot and its surrounding area is extracted in the range to ensure complete coverage of the terrain change around the building; Step S162, the DSM data extracted in the buffer zone is analyzed by the probability density estimation method, the distribution model of the elevation data is constructed, and the data is optimized by the quantile truncated mean method; Wherein, the step S161 specifically is: If the building plot buffer is , the buffer radius is d, and its geometric expression is: ; wherein (x, y) represent the horizontal and vertical coordinate values in the image coordinate system of any pixel in the buffer r , y r ) represent the horizontal and vertical coordinate values in the image coordinate system of the center pixel of the building plot ​​ Extracting elevation data sets within buffer ranges : ; wherein z i represents a buffer image coordinates (x i , y i ) in the DSM data; In the step S162, the data is optimized by the quantile truncated mean method, comprising: all extracted elevation values are arranged in order from small to large, and the elevation values in the 5%-10% interval are selected for mean value calculation, which is used as the building ground elevation, specifically: The elevation values extracted in the buffer are sorted from small to large, denoted as ; select 5%-10% of the elevation values in the interval, and calculate the mean value as the ground elevation value: wherein - +1; wherein the elevation value in the interval of 5% to 10% is selected by using the probability density estimation constructing a distribution model of the elevation data: based on analyzing the distribution form of the distribution model, the elevation value in the interval of 5% to 10% is selected where, is the kernel function, commonly used Gaussian kernel: , is the bandwidth, which determines the smoothness of the distribution, and is determined according to historical experience values or experimental data.

2. The method of claim 1, wherein, The step S12 comprises: Step S121, radiation correction processing: using the radiation calibration module and atmospheric correction module in the remote sensing image processing and analysis software ENVI, the rear view image data of resource three satellite stereo pair is sequentially subjected to radiation calibration and atmospheric correction to eliminate the influence of atmospheric scattering and absorption on radiation; Step S122, ortho correction processing: using the ortho correction module in the remote sensing image processing and analysis software ENVI, the rear view image map subjected to radiation correction processing in step S121 is subjected to ortho correction to eliminate the geometric distortion caused by terrain undulation, and the satellite rear view image base map is obtained.

3. The method of claim 1, wherein, The step S13 comprises: Step S131, on the satellite rear view image base map, using the supervised classification module in the remote sensing image processing and analysis software ENVI, selecting appropriate building samples, using the object-oriented classification method, classifying and extracting the building graph spot, and obtaining the building graph spot vector. Step S132, in combination with the satellite rear view image base map, in the geographic information system software ArcGIS Pro software, using the artificial visual correction method, the building graph spot vector is edited and corrected manually to obtain the building two-dimensional contour data.

4. The method of claim 1, wherein, The step S14 comprises: Step S141, satellite stereo data import: using the SAT Master 10.0 satellite processing module in the photogrammetry and remote sensing data processing software Trimble Inpho Photogrammetry 10.0, through at least including creating project, importing the front view image data and rear view image data, importing RPC information, creating scene, project checking steps, completing the import of satellite stereo image pair data; Step S142, project image preprocessing: opening the project created in step S141, at least including image pyramid creation, image radiation correction processing operation, completing project processing setting; Step S143, aerial triangulation processing: using the MATCH-AT satellite triangulation module in the Trimble Inpho Photogrammetry 10.0 software, automatically identifying the same name image points in multiple satellite stereo image pairs, and extracting these points as the basic control data of aerial triangulation; and using the results of aerial triangulation, iteratively optimizing the strict geometric model parameters RPCs of satellite image to improve the geographical positioning accuracy of image; Step S144, generating DSM data: using the Match-TDSM module in the Trimble Inpho Photogrammetry 10.0 software, using the corrected RPCs, combining the same name image points matched by multi-view image, generating the DSM data containing the building roof height.

5. The method of claim 1, wherein, The step S15 comprises: Step S151, according to the building two-dimensional contour data, based on the geographic information system software ArcGIS Pro software, obtaining the elevation value of each building area pixel from the DSM data; Step S152, using the quantile truncation method, sorting the DSM elevation data in the building graph spot area from small to large, and selecting the elevation value at the 98% quantile as the roof elevation to effectively remove the noise and abnormal elevation values that may exist in the DSM data.

6. The method of claim 5, wherein, The step S151 specifically comprises: If the building plot is contains The corresponding elevation value of each pixel is The elevation value extraction formula is: Wherein, 1≤i≤n, is the coordinate of each pixel in the DSM; The step S152 specifically comprises: Let the sorted elevation values be denoted as , define the score as: where p represents the quantile proportion, and the value range ; Taking the 98th percentile of the elevation values Representing the roof elevation: .

7. The method of claim 1, wherein, The step S17 specifically comprises: In the geographic information system software ArcGIS Pro software, the difference value between the numerical value corresponding to the building roof elevation field and the numerical value corresponding to the building ground elevation field in the building graph spot vector data attribute table is calculated to obtain the height data of the building graph spot.

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