Building height extraction method, device, equipment, medium and program product

Abstract

The invention discloses a building height extraction method, device and equipment, a medium and a program product, and the method comprises the steps: carrying out the matching positioning of a remote sensing satellite image of a target region through a digital elevation model and a digital orthoimage of the target region; then obtaining the bottom elevation of a target building in the target area through the digital elevation model; according to the bottom elevation, the vector contour of a target building in a digital line map is registered with the digital orthoimage and the remote sensing satellite image after matching and positioning, and the absolute roof elevation of the target building is obtained; and finally, determining the building height of the target building according to the bottom elevation and the absolute roof elevation, thereby realizing accurate registration of the vector contour of the building and improving the automatic extraction accuracy of the building height data of the building.

Description

Technical Field

[0001] The present invention relates to the field of wireless technology, and in particular to a method, device, equipment, medium and program product for extracting the height of a building. Background Art

[0002] For the application of network optimization three-dimensional map data, building height data is particularly important in geographic information data. At present, existing technologies mainly obtain the elevation values ​​of building vertices and ground elevation values ​​through digital surface models (Digital Surface Model, DSM), or construct buildings in satellite images through image recognition to calculate the elevation values ​​of the bottom point and the top point of the building, and then calculate the difference to obtain the building height. However, due to the limitations of image recognition technology and objective factors, the construction of buildings through image recognition is not only unable to accurately identify individual buildings, but also has the defect of difficulty in identifying the bottom point and the top point of the building. The elevation value obtained through DSM is generally the average elevation value inside the building outline. In addition, due to the limitations of dense matching of satellite stereo image pairs, DSM obtained in urban areas is difficult to accurately represent the shape of buildings, resulting in a large error between the roof elevation value of each building and the true value, making it impossible to accurately extract the building height. Summary of the Invention

[0003] In response to the problems existing in the prior art, embodiments of the present invention provide a building height extraction method, device, equipment, medium and program product, which can achieve precise alignment of building vector contours and improve the accuracy of automatic extraction of building height data.

[0004] In a first aspect, an embodiment of the present invention provides a method for extracting building heights, comprising:

[0005] Matching and positioning the remote sensing satellite image of the target area based on the digital elevation model and digital orthophoto of the target area;

[0006] Obtaining the bottom elevation of a target building in the target area through the digital elevation model;

[0007] According to the bottom elevation, the vector outline of the target building in the digital line map is aligned with the digital orthophoto and the remote sensing satellite image after matching and positioning to obtain the absolute elevation of the roof of the target building;

[0008] The height of the target building is determined according to the bottom elevation and the absolute roof elevation.

[0009] As an improvement to the above solution, according to the bottom elevation, the vector outline of the target building in the digital line map is registered with the digital orthophoto and the remote sensing satellite image after matching and positioning to obtain the absolute elevation of the roof of the target building, including:

[0010] According to the bottom elevation, forward matching is performed between the vector outline of the target building in the digital line map and the digital orthophoto to obtain a first roof elevation of the target building;

[0011] According to the bottom elevation, reverse matching is performed on the forward matching vector outline of the target building and the remote sensing satellite image after matching and positioning to obtain a second roof elevation of the target building; wherein the forward matching vector outline is obtained after forward matching is completed between the vector outline of the target building in the digital line map and the digital orthophoto;

[0012] The absolute roof elevation of the target building is determined according to the first roof elevation and the second roof elevation.

[0013] As an improvement to the above solution, forward matching the vector outline of the target building in the digital line map with the digital orthophoto according to the bottom elevation to obtain the first roof elevation of the target building includes:

[0014] Overlaying the vector outline of the target building in the digital line map with the digital orthophoto to obtain a grayscale reference template and a mask template of the target building;

[0015] Performing a roof elevation search cycle according to the bottom elevation to obtain an elevation sampling target template outputted in each cycle;

[0016] Determine the correlation coefficient between the grayscale reference template and the elevation sampling target template outputted in each cycle according to the grayscale reference template, the mask template and the elevation sampling target template outputted in each cycle;

[0017] A first roof elevation of the target building is determined according to the correlation coefficient.

[0018] As an improvement to the above solution, performing a roof elevation search cycle according to the bottom elevation to obtain an elevation sampling target template outputted in each cycle includes:

[0019] Taking the bottom elevation as the initial value, a roof elevation search cycle is performed according to a set incremental step size;

[0020] In each roof elevation search cycle, the contour vector node corresponding to the target building is projected onto the matched and positioned remote sensing satellite image according to the elevation value searched in the current cycle, and the projection position of the corresponding contour vector node on the matched and positioned remote sensing satellite image is obtained;

[0021] According to the projection position, grayscale value sampling is performed on the remote sensing satellite image after matching and positioning, and an elevation sampling target template corresponding to the elevation value searched in the current cycle is generated.

[0022] As an improvement to the above solution, determining the first roof elevation of the target building according to the correlation coefficient includes:

[0023] Comparing the correlation coefficients between the grayscale reference template and the elevation sampling target template outputted in each cycle;

[0024] The elevation value corresponding to the maximum correlation coefficient is obtained as the first roof elevation of the target building.

[0025] As an improvement to the above solution, determining the correlation coefficient between the grayscale reference template and the elevation sampling target template outputted in each cycle according to the grayscale reference template, the mask template, and the elevation sampling target template outputted in each cycle includes:

[0026] For the elevation sampling template output in each cycle, calculating the first average value of the pixel grayscale in the elevation sampling target template;

[0027] Calculating a second average value of pixel grayscales in the grayscale reference template;

[0028] The correlation coefficient between the grayscale reference template and the corresponding elevation sampling target template is calculated based on the first average value, the second average value, the grayscale values ​​of each pixel in the elevation sampling target template, the grayscale values ​​of each pixel in the grayscale reference template, and the mask values ​​in the mask template.

[0029] As an improvement to the above solution, projecting the contour vector node of the target building corresponding to the elevation value found in the current loop search onto the matched and positioned remote sensing satellite image to obtain the projection position of the corresponding contour vector node on the matched and positioned remote sensing satellite image includes:

[0030] Transforming the geographic coordinates of the contour vector node of the target building corresponding to the elevation value searched in the current cycle into the image coordinate system of the digital orthophoto to obtain the first transformed coordinates of the corresponding contour vector node;

[0031] translating the first transformed coordinates of each of the contour vector nodes to the local image coordinate system of the grayscale reference template to obtain the first image coordinates of each of the contour vector nodes on the grayscale reference template;

[0032] The first image coordinates of each of the contour vector nodes are reprojected onto the matched and positioned remote sensing satellite image according to the rational function model and orientation parameters of the remote sensing satellite image to obtain the projection position of each of the contour vector nodes on the matched and positioned remote sensing satellite image.

[0033] As an improvement to the above solution, reprojecting the first image coordinates of each of the contour vector nodes onto the matched and positioned remote sensing satellite image according to the rational function model and orientation parameters of the remote sensing satellite image to obtain the projection position of each of the contour vector nodes on the matched and positioned remote sensing satellite image includes:

[0034] Calculating RPC projection coordinates of the first image coordinates on the remote sensing satellite image according to the rational function model of the remote sensing satellite image according to the first image coordinates;

[0035] transforming the RPC projection coordinates into the image coordinate system of the remote sensing satellite image according to the orientation parameters of the remote sensing satellite image, to obtain a mapping relationship between the grayscale reference template and the remote sensing satellite image;

[0036] According to the mapping relationship, the first image coordinates of each of the outline vector nodes are transformed to the matched and positioned remote sensing satellite image to obtain the projection position of each of the outline vector nodes on the matched and positioned remote sensing satellite image.

[0037] As an improvement to the above solution, matching and positioning the remote sensing satellite image of the target area based on the digital elevation model and the digital orthophoto of the target area includes:

[0038] Automatically registering the remote sensing satellite image and the digital orthophoto image to obtain homonymous points and their spatial plane coordinates;

[0039] The remote sensing satellite image is automatically oriented by interpolating the spatial plane coordinates of the points of the same name in the digital elevation model.

[0040] In a second aspect, an embodiment of the present invention provides a device for extracting building height, comprising:

[0041] A matching and positioning module is used to match and position the remote sensing satellite image of the target area based on the digital elevation model and digital orthophoto of the target area;

[0042] A bottom elevation acquisition module, configured to acquire the bottom elevation of a target building in the target area using the digital elevation model;

[0043] A roof absolute elevation acquisition module is used to align the vector outline of the target building in the digital line map with the digital orthophoto and the matched and positioned remote sensing satellite image based on the bottom elevation to obtain the absolute elevation of the roof of the target building;

[0044] The building height determination module is used to determine the height of the target building according to the bottom elevation and the absolute roof elevation.

[0045] In a third aspect, an embodiment of the present invention provides a building height extraction device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the building height extraction method as described in any one of the first aspects.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the building height extraction method as described in any one of the first aspects.

[0047] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the building height extraction method as described in any one of the first aspects.

[0048] Compared with the prior art, the embodiments of the present invention provide a building height extraction method, device, equipment, medium and program product, which match and locate the remote sensing satellite image of the target area through the digital elevation model and digital orthophoto of the target area; then obtain the bottom elevation of the target building in the target area through the digital elevation model; based on the bottom elevation, align the vector outline of the target building in the digital line map with the digital orthophoto and the matched and located remote sensing satellite image to obtain the absolute elevation of the roof of the target building; finally, determine the height of the target building based on the bottom elevation and the absolute elevation of the roof, thereby achieving precise alignment of the building vector outline and improving the accuracy of automatic extraction of building height data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings used in the implementation methods. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 This is a flow chart of a building height extraction method provided by an embodiment of the present invention;

[0051] Figure 2 is another flow chart of a method for extracting building heights provided by an embodiment of the present invention;

[0052] Figure 3 is a remote sensing satellite image before Wallis transformation provided by an embodiment of the present invention;

[0053] Figure 4 is a remote sensing satellite image after Wallis transformation provided by an embodiment of the present invention;

[0054] Figure 5 Schematic diagram of extracting a grayscale reference template and a mask template from a digital orthophoto provided by an embodiment of the present invention;

[0055] Figure 6 Schematic diagram of the coordinate system of the outline vector nodes of the target building on the grayscale reference template and the digital orthophoto provided by the embodiment of the present invention;

[0056] Figure 7 is a schematic diagram of forward matching provided by an embodiment of the present invention;

[0057] Figure 8 is a schematic diagram of reverse matching provided by an embodiment of the present invention;

[0058] Figure 9 This is a structural block diagram of a building height extraction device provided by an embodiment of the present invention;

[0059] Figure 10 This is a structural block diagram of a building height extraction device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] See Figure 1, Figure 1 Flowchart of a method for extracting building height provided by an embodiment of the present invention. The method for extracting target building height specifically includes:

[0062] S1: Matching and positioning the remote sensing satellite image of the target area according to the digital elevation model and digital orthophoto of the target area;

[0063] S2: Obtaining the bottom elevation of the target building in the target area through the digital elevation model;

[0064] S3: Based on the bottom elevation, register the vector outline of the target building in the digital line map with the digital orthophoto and the matched and positioned remote sensing satellite image to obtain the absolute elevation of the roof of the target building;

[0065] S4: Determine the height of the target building according to the bottom elevation and the absolute roof elevation.

[0066] It should be noted that the building height extraction method described in the embodiment of the present invention can be implemented by a computing device with computing and image processing functions such as a server or a computer. The detailed process of the above steps S1 to S4 can be referred to Figure 2 , Figure 2 This is another flow chart of a building height extraction method provided by an embodiment of the present invention. In this embodiment of the present invention, the automatic extraction process of the target building height mainly includes remote sensing satellite imagery (High Resolution Satellite Imagery) matching positioning and vector alignment of the building vector contour. After the above processing, the target building height is output.

[0067] Remote sensing satellite image matching and positioning requires first collecting the digital elevation model (DEM), digital orthophoto map (DOM) and remote sensing satellite image of the target area; among them, the remote sensing satellite image is a single-view remote sensing satellite image, and the target area refers to the ground area containing the target building; then the digital elevation model and digital orthophoto map are used as geographic reference data for remote sensing satellite image orientation to match and position the remote sensing satellite image; among them, the digital elevation model provides elevation control data for remote sensing satellite image orientation, and the digital orthophoto map provides plane control data for remote sensing satellite image orientation.

[0068] Considering that the imaging process of remote sensing satellite images is affected by factors such as weather conditions, atmospheric conditions, and camera response, the captured remote sensing satellite images have a certain degree of radiometric distortion, which adversely affects remote sensing satellite image matching and positioning. In remote sensing satellite image matching and positioning, embodiments of the present invention consider improving the radiometric quality of images. Before matching and positioning, remote sensing satellite images are preprocessed, specifically by enhancing the brightness and contrast of remote sensing satellite images.

[0069] For example, a Wallis transform is performed on a remote sensing satellite image to enhance its brightness and contrast. The Wallis transform not only forces the average brightness and contrast of the remote sensing satellite image to be changed to a preset brightness and contrast, but also is a local transform that can adaptively enhance different regions of the remote sensing satellite image. The general form of the Wallis transform is:

[0070]

[0071] Among them, g W (x, y) and g(x, y) are the grayscale value of the coordinate (x, y) after Wallis transformation and the grayscale value of the remote sensing satellite image before transformation, respectively. g and s g are the mean and standard deviation of the image grayscale in the local window, m f and s f are the grayscale mean and grayscale standard deviation values ​​that the remote sensing satellite image in the local window needs to achieve after the transformation, c is the contrast stretching constant, b is the brightness constant, r1 and r2 represent the transformation coefficients. In the embodiment of the present invention, the size of the local window is set to 31×31 pixels, and in order to prevent saturation effect, the contrast stretching constant b is set to 0.6 and the brightness constant c is usually set to 0.75. The remote sensing satellite image before Wallis transformation is as follows Figure 3 As shown in the figure, the remote sensing satellite image after Wallis transformation is as follows Figure 4 As shown in Figure 2, the Wallis transform processing can effectively improve the matching and positioning effect of remote sensing satellite images.

[0072] The vector registration of the building vector outline requires first obtaining the bottom elevation of the target building in the target area through the digital elevation model; then, based on the bottom elevation, the vector outline of the target building in the digital line map is registered with the digital orthophoto and the remote sensing satellite image after matching and positioning to obtain the absolute elevation of the roof of the target building; finally, the absolute elevation of the roof is subtracted from the bottom elevation to obtain the height of the target building. The embodiment of the present invention matches the building as a whole. Compared with the prior art that matches through a single pixel point, the embodiment of the present invention only needs to use the digital elevation model, digital orthophoto and remote sensing satellite image (single view) to calculate the height of the building based on the vector outline of the building, thereby improving the matching accuracy and precision, thereby improving the accuracy of automatic extraction of building height data and realizing automatic matching of the target building. At the same time, it does not rely on satellite stereo image pairs, reducing the data cost of collecting building height data.

[0073] Specifically, step S1: matching and positioning the remote sensing satellite image of the target area according to the digital elevation model and the digital orthophoto of the target area, including:

[0074] Automatically registering the remote sensing satellite image and the digital orthophoto image to obtain homonymous points and their spatial plane coordinates;

[0075] The remote sensing satellite image is automatically oriented by interpolating the spatial plane coordinates of the points of the same name in the digital elevation model.

[0076] Considering that most of the systematic errors in the satellite attitude and orbit parameters have been eliminated after the on-orbit geometric calibration of the remote sensing satellite image by the ground data processing system, the provided RPC parameter file can be used for ground positioning. However, there is still a certain degree of systematic error in these RPC parameters. The embodiment of the present invention matches and positions the remote sensing satellite image. For example, the pre-processed remote sensing satellite image is automatically aligned with the digital orthophoto to obtain the same-name points; then, the spatial plane coordinates of the corresponding same-name points are obtained through the geographic information data of the digital orthophoto, and then the elevations of the corresponding same-name points are obtained by interpolating the spatial plane coordinates of the same-name points in the digital elevation model, so that the corresponding same-name points become the image control points for the orientation of the pre-processed remote sensing satellite image; finally, these image control points are used to orient the pre-processed remote sensing satellite image, and the error in the re-projection of the image control points on the remote sensing satellite image after the stone bench orientation meets the predetermined threshold requirement, thereby achieving high-precision orientation of the remote sensing satellite image.

[0077] In an embodiment of the present invention, remote sensing satellite images and digital orthophotos are automatically aligned by feature extraction operators with scale, rotation, and translation, such as SIFT operators, SURF operators, etc., to determine the same-name points between the remote sensing satellite images and the digital orthophotos. For example, principal component analysis (PCA) can be used to reduce the dimension of the feature vectors of the feature points of the remote sensing satellite images and the digital orthophotos, and then a fast nearest neighbor algorithm (such as a KD tree or a hash algorithm, etc.) can be used to improve the matching speed of the same-name points. It should be noted that the use of SIFT operators, SURF operators, principal component analysis algorithms, and fast nearest neighbor algorithms for automatic alignment of remote sensing satellite images and digital orthophotos belongs to the prior art and will not be described in detail here.

[0078] Remote sensing satellite image positioning is generally based on the rational function model (RFM) positioning. Based on the RPC (Rational Polynomial Coefficient) parameters of the remote sensing satellite image, an affine transformation is defined in the remote sensing satellite image.

[0079]

[0080] Among them, the coordinates (x1, y1) are the coordinates obtained by projecting the image control point onto the remote sensing satellite image according to the rational function model, (x2, y2) are the measured coordinates of the image control point on the remote sensing satellite image, and (A0, A1, A2; B0, B1, B2) are the orientation parameters of the remote sensing satellite image that need to be solved.

[0081] It should be noted that, in the embodiment of the present invention, each building is processed independently, that is, the bottom elevation acquisition process of step S2 and the roof absolute elevation acquisition process of step S3 are independently executed for each building.

[0082] Specifically, step S2: obtaining the bottom elevation of the target building in the target area through the digital elevation model includes:

[0083] Interpolate on the digital elevation model to obtain the bottom elevation value of each node on the vector outline of the target building;

[0084] A minimum bottom elevation value is extracted from the bottom elevation values ​​as the final bottom elevation of the target building.

[0085] Specifically, step S3: based on the bottom elevation, registering the vector outline of the target building in the digital line map with the digital orthophoto and the matched and positioned remote sensing satellite image to obtain the absolute roof elevation of the target building, including:

[0086] S31: forward matching the vector outline of the target building in the digital line map with the digital orthophoto according to the bottom elevation to obtain a first roof elevation of the target building;

[0087] S32: Based on the bottom elevation, reverse matching is performed on the forward matching vector outline of the target building and the remote sensing satellite image after matching and positioning to obtain a second roof elevation of the target building; wherein the forward matching vector outline is obtained by forward matching the vector outline of the target building in the digital line map with the digital orthophoto;

[0088] S33: Determine the absolute roof elevation of the target building according to the first roof elevation and the second roof elevation.

[0089] A building is selected as the target building from the input digital line graphic (DLG) of the target area. The vector outline of the target building is overlaid with the digital orthophoto for forward matching, completing the matching process from the digital orthophoto to the remote sensing satellite image. The vector outline of the target building is overlaid with the remote sensing satellite image for reverse matching, completing the matching process from the remote sensing satellite image to the digital orthophoto. The absolute roof elevation of the target building is determined based on the first roof elevation obtained in the forward matching process and the second roof elevation obtained in the reverse matching process.

[0090] Further, step S31: forward matching the vector outline of the target building in the digital line map with the digital orthophoto according to the bottom elevation to obtain the first roof elevation of the target building, including:

[0091] S311: Overlaying the vector outline of the target building in the digital line map with the digital orthophoto to obtain a grayscale reference template and a mask template of the target building;

[0092] For example, the forward matching is based on the digital orthophoto as the target, and the pixel gray value of the image is sampled from the digital orthophoto according to the circumscribed rectangle of the vector outline of the target building to generate a gray reference template G c ; At the same time, make a grayscale reference template G c A mask template of the same size; wherein each pixel in the mask template has a value of 0 or 1, where the pixel is 1 when it is inside the vector contour, and 0 otherwise.

[0093] S312: performing a roof elevation search cycle according to the bottom elevation, and obtaining an elevation sampling target template outputted in each cycle;

[0094] S313: determining a correlation coefficient between the grayscale reference template and the elevation sampling target template outputted in each cycle according to the grayscale reference template, the mask template, and the elevation sampling target template outputted in each cycle;

[0095] S314: Determine a first roof elevation of the target building according to the correlation coefficient.

[0096] The embodiment of the present invention uses existing vector map data (i.e., digital line map) to construct an elevation sampling target template by directly obtaining the target building roof image from the digital orthophoto after overlaying the vector outline of the target building with the digital orthophoto, thereby avoiding the defect that the image recognition algorithm cannot accurately identify the building outline.

[0097] Further, step S312: performing a roof elevation search cycle according to the bottom elevation to obtain an elevation sampling target template outputted in each cycle, including:

[0098] Taking the bottom elevation as the initial value, a roof elevation search cycle is performed according to a set incremental step size;

[0099] In each roof elevation search cycle, the contour vector node corresponding to the target building is projected onto the matched and positioned remote sensing satellite image according to the elevation value searched in the current cycle, and the projection position of the corresponding contour vector node on the matched and positioned remote sensing satellite image is obtained;

[0100] According to the projection position, grayscale value sampling is performed on the remote sensing satellite image after matching and positioning, and an elevation sampling target template corresponding to the elevation value searched in the current cycle is generated.

[0101] Specifically, projecting the contour vector node of the target building corresponding to the elevation value searched in the current cycle onto the matched and positioned remote sensing satellite image to obtain the projection position of the corresponding contour vector node on the matched and positioned remote sensing satellite image includes:

[0102] Transforming the geographic coordinates of the contour vector node of the target building corresponding to the elevation value searched in the current cycle into the image coordinate system of the digital orthophoto to obtain the first transformed coordinates of the corresponding contour vector node;

[0103] translating the first transformed coordinates of each of the contour vector nodes to the local image coordinate system of the grayscale reference template to obtain the first image coordinates of each of the contour vector nodes on the grayscale reference template;

[0104] The first image coordinates of each of the contour vector nodes are reprojected onto the matched and positioned remote sensing satellite image according to the rational function model and orientation parameters of the remote sensing satellite image to obtain the projection position of each of the contour vector nodes on the matched and positioned remote sensing satellite image.

[0105] The process of obtaining the projection position of each of the outline vector nodes on the remote sensing satellite image after matching and positioning includes the following steps:

[0106] Calculating RPC projection coordinates of the first image coordinates on the remote sensing satellite image according to the rational function model of the remote sensing satellite image according to the first image coordinates;

[0107] transforming the RPC projection coordinates into the image coordinate system of the remote sensing satellite image according to the orientation parameters of the remote sensing satellite image, to obtain a mapping relationship between the grayscale reference template and the remote sensing satellite image;

[0108] According to the mapping relationship, the first image coordinates of each of the outline vector nodes are transformed to the matched and positioned remote sensing satellite image to obtain the projection position of each of the outline vector nodes on the matched and positioned remote sensing satellite image.

[0109] For example, a roof elevation search loop is started with the bottom elevation of the target building obtained in step S2, and a set incremental step size is added in each loop to output the elevation value of the current loop; wherein the incremental step size is determined according to the accuracy requirements for the building height in actual applications, for example, the incremental step size is 1 / 2 or 1 / 3 of the building height accuracy requirements.

[0110] In each loop, the output height value z k , k represents the number of cycles, projecting the outline vector nodes of the target building onto the remote sensing satellite image that has been oriented and has met the accuracy requirements (i.e., completing step 1 above);

[0111] According to the order of the contour vector nodes, the corresponding contour vector nodes are extracted in the grayscale reference template G c The first image coordinate (x i ,y i ) and its projected position on the remote sensing satellite image (x′ i , y′ i ), i = 1, 2, .., n, n is the number of nodes of the outline vector of the target building.

[0112] Among them, the first image coordinate (x i ,y i ) is the outline vector node of the target building in the grayscale reference template G c The local image coordinate system (such as Figure 6 The specific process is as follows:

[0113] According to the geographic coordinates of the contour vector nodes, the image coordinate system of the digital orthophoto is converted (Ox dom y dom )

[0114] Translate the converted coordinates to the grayscale reference template G c The local image coordinate system (o-xy) is used to obtain the first image coordinate (x i ,y i ).

[0115] Projection position (x′ i , y′ i ) is the spatial coordinate of the contour vector node (X i , Y i , Z i ) The coordinates are obtained by reprojecting the image onto the remote sensing satellite image according to the rational function model and orientation parameters of the remote sensing satellite image. The specific process is as follows:

[0116] According to the first image coordinate (x i ,y i ) and the rational function model to calculate the RPC projection coordinates (samp(column), line(row)) of the remote sensing satellite image:

[0117]

[0118] Among them, P1, P2, P3, and P4 are polynomials with the geographic coordinates of the ground point (Latitude, Longitude, and Height) as independent variables. (r n ,co n ) is the image coordinate after the RPC projection coordinate (samp, line) is normalized, (P n ,L n ,H n ) is the ground coordinate after normalization of the geographic coordinates (Latitude, Longitude, Height) of the ground point.

[0119] NumL(P n ,L n ,H n )=a0+a1L n +a2P n +a3H n +a4L n P n +a5L n H n +a6P n Hn

[0120] +a7L n 2 +a8P n 2 +a9H n 2 +a 10 P n L n H n +a 11 L n 3 +a 12 L n P n 2

[0121] +a 13 L n H n 2 +a 14 L n 2 P n +a 15 P n 3 +a 16 P n H n 2 +a 17 L n 2 H n

[0122] +a 18 P n 2 H n +a 19 H n 3 (4);

[0123] DenL(P n ,L n ,H n )=b0+b1L n +b2P n +b3H n +b4L n P n +b5L n H n +b6P n H n

[0124] +b7L n 2 +b8P n2 +b9H n 2 +b 10 P n L n H n +b 11 L n 3 +b 12 L n P n 2

[0125] +b 13 L n H n 2 +b 14 L n 2 P n +b 15 P n 3 +b 16 P n H n 2 +b 17 L n 2 H n

[0126] +b 18 P n 2 H n +b 19 H n 3 (5);

[0127] NumS(P n ,L n ,H n )=c0+c1L n +c2P n +c3H n +c4L n P n +c5L n H n +c6P n H n

[0128] +c7L n 2 +c8P n 2 +c9H n 2 +c 10 P n L n Hn +c 11 L n 3 +c 12 L n P n 2

[0129] +c 13 L n H n 2 +c 14 L n 2 P n +c 15 P n 3 +c 16 P n H n 2 +c 17 L n 2 H n

[0130] +c 18 P n 2 H n +c 19 H n 3 (6);

[0131] NumS(P n ,L n ,H n )=d0+d1L n +d2P n +d3H n +d4L n P n +d5L n H n +d6P n H n

[0132] +d7L n 2 +d8P n 2 +d9H n 2 +d 10 P n L n H n +d 11 L n 3 +d 12 L n Pn 2

[0133] +d 13 L n H n 2 +d 14 L n 2 P n +d 15 P n 3 +d 16 P n H n 2 +d 17 L n 2 H n

[0134] +d 18 P n 2 H n +d 19 H n 3 (7);

[0135] Among them, the coefficient a of the above polynomial j , b j , c j , d j , (0≤j≤19) is the preset coefficient of the rational function model.

[0136]

[0137] Among them, LINE_OFF, SAMP_OFF, LONG_OFF, LAT_OFF, and HEIGHT_OFF are regularized translation parameters, representing the row offset relative to the image center point, the column offset relative to the image center point, the geodetic longitude offset, the geodetic latitude offset, and the geodetic height offset, respectively. LINE_SCALE, SAMP_SCALE, LONG_SCALE, LAT_SCALE, and HEIGHT_SCALE are regularized scale parameters, representing the row scale, column scale, geodetic longitude scale, geodetic latitude scale, and geodetic height scale, respectively.

[0138] Then, according to the orientation parameters (A0, A1, A2; B0, B1, B2) of the remote sensing satellite image calculated above, the RPC projection coordinates (samp i ,line o ) is converted to the image coordinates (x′) on the remote sensing satellite image i ,y′ i), i.e., the projected position on the remote sensing satellite image;

[0139]

[0140] According to n pairs of node coordinates (i.e., RPC projection coordinates and projection positions corresponding to n pairs of contour vector nodes), the affine transformation parameters are calculated using the least squares method:

[0141]

[0142] The above formula (10) gives the grayscale reference template G c The mapping relationship between the first image coordinate (x, y) on the remote sensing satellite image and the projection position (x′, y′) on the remote sensing satellite image is used to map the grayscale reference template G c Substituting the first image coordinate of each pixel in into formula (10) can obtain the projection position of the pixel on the remote sensing satellite image.

[0143] Then, based on the projection position obtained above, the grayscale value of the corresponding contour vector node is sampled on the remote sensing satellite image to generate an elevation value z corresponding to the current loop output. k , and with the grayscale reference template G c The height sampling target template G' of the same size is as follows: Figure 7 shown.

[0144] Further, step S313: determining the correlation coefficient between the grayscale reference template and the elevation sampling target template outputted in each cycle according to the grayscale reference template, the mask template and the elevation sampling target template outputted in each cycle, including:

[0145] For the elevation sampling template output in each cycle, calculating the first average value of the pixel grayscale in the elevation sampling target template;

[0146] Calculating a second average value of pixel grayscales in the grayscale reference template;

[0147] The correlation coefficient between the grayscale reference template and the corresponding elevation sampling target template is calculated based on the first average value, the second average value, the grayscale values ​​of each pixel in the elevation sampling target template, the grayscale values ​​of each pixel in the grayscale reference template, and the mask values ​​in the mask template.

[0148] For example, obtain the grayscale reference template G c , mask template m(c, r) and the height sampling target template G' to be matched, calculate the grayscale reference template G according to the following formula c The correlation coefficient N between the elevation sampling target template G' CC (z).

[0149]

[0150] in,

[0151]

[0152] In the above formula, M, N are grayscale reference templates G c The number of rows and columns, G c (c′, r′) is the grayscale reference template G c The grayscale value of the pixel in the c′th column and r′th row, is the grayscale reference template G c The second average value of the pixel grayscale in G'(c',r') is the pixel grayscale value of the pixel in the c'th column and r'th row in the elevation sampling target template G'. is the first average value of the pixel grayscale in the elevation sampling target template G', and m(c', r') is the mask value of the pixel in the c'th column and r'th row in the mask template.

[0153] Further, step S314: determining the first roof elevation of the target building according to the correlation coefficient, including:

[0154] Comparing the correlation coefficients between the grayscale reference template and the elevation sampling target template outputted in each cycle;

[0155] The elevation value corresponding to the maximum correlation coefficient is obtained as the first roof elevation of the target building.

[0156] For example, after completing the roof elevation search cycle, the correlation coefficient N between the grayscale reference template and the elevation sampling target template output by each cycle is calculated. CC Compare and select the correlation coefficient N CC The maximum elevation value is used as the first roof elevation of the target building roof in the forward matching from the digital orthophoto to the remote sensing satellite image.

[0157] In an embodiment of the present invention, a plumb line object space matching method is adopted in the first roof elevation calculation process, that is, the elevation is iteratively searched step by step starting from the elevation of the bottom of the building. This method uses similarity transformation to resample the remote sensing satellite image to eliminate the influence of rotation and scale changes between the digital orthophoto image and the remote sensing satellite image, which can improve the matching accuracy; at the same time, by making a mask template of the target building grayscale reference template and applying it in the correlation coefficient calculation process, the influence of grayscale other than the target building roof outline can be eliminated, thereby improving the matching accuracy.

[0158] Furthermore, step S32: based on the bottom elevation, reverse matching is performed on the forward matching vector outline of the target building and the remote sensing satellite image after matching and positioning to obtain a second roof elevation of the target building, including:

[0159] Fitting the vector outline of the target building with the remote sensing satellite image to obtain a reverse matching grayscale reference template and a reverse matching mask template;

[0160] Performing a roof elevation search cycle according to the bottom elevation to obtain a reverse matching elevation sampling target template outputted in each cycle;

[0161] Determine a reverse matching correlation coefficient between the reverse matching grayscale reference template and the reverse matching elevation sampling target template outputted in each cycle according to the reverse matching grayscale reference template, the reverse matching mask template and the reverse matching elevation sampling target template outputted in each cycle;

[0162] A second roof elevation of the target building is determined according to the reverse matching correlation coefficient.

[0163] Furthermore, performing a roof elevation search cycle according to the bottom elevation to obtain a reverse matching elevation sampling target template outputted in each cycle includes:

[0164] According to the orientation parameters and imaging geometry model of the remote sensing satellite image, the coordinates of the corner points of the circumscribed rectangle of the vector outline of the target building on the remote sensing satellite image are projected onto the elevation plane corresponding to the elevation value output in each cycle to obtain the second image coordinates of the corner point coordinates on the digital orthophoto;

[0165] According to the coordinates of the second image, a corresponding reverse matching elevation sampling target template is generated.

[0166] It should be noted that the matching process from remote sensing satellite imagery to digital orthophotos uses the remote sensing satellite imagery as the target to generate the corresponding reverse matching grayscale reference template and reverse matching mask template. The principles behind this are the same as those for generating the grayscale reference template and mask template in the forward matching process described above. Similarly, the principles for the roof elevation search loop, generating the correlation coefficient, and determining the second roof elevation based on the correlation coefficient in the reverse matching process are also the same as those in the forward matching process described above, and will not be further elaborated here. The incremental step size of the roof elevation search loop in the reverse matching process is the same as that in the forward matching process.

[0167] Then, according to the orientation parameters and imaging geometry model of the remote sensing satellite image, the corner coordinates (Xi ,Y i ) projected onto the elevation plane z k , k=1,2,...n z ;n z is the number of steps of elevation search, and is converted into the second image coordinate (X′ i ,Y′ i ), i = 1, 2, ..., 4, representing the sequence number of the corner coordinates; the affine transformation parameters are calculated based on these 4 pairs of coordinates:

[0168]

[0169] According to the affine transformation parameters calculated by the above formula, a reverse matching elevation sampling target template with the same size as the reverse matching grayscale reference template is generated from the digital orthophoto upsampling, as shown in Figure 8 As shown. It can be understood that if and only if the height z k When the true elevation of the target building is the contour vector V on the remote sensing satellite image, sat The projected digital orthophoto can be compared with the original contour vector V dlg The embodiment of the present invention adopts a strict imaging geometry model of remote sensing satellite images to improve the mathematical rigor of building roof elevation calculation.

[0170] After obtaining the first roof elevation obtained by the forward matching solution and the second roof elevation obtained by the reverse matching solution, the absolute value of the difference between the first roof elevation and the second roof elevation is calculated;

[0171] When the absolute value of the difference is less than the accuracy requirement for the building height in actual applications, the average of the first roof elevation and the second roof elevation is calculated as the absolute roof elevation of the target building.

[0172] It should be noted that the accuracy requirements for building heights in actual applications can be customized by the user and are not specifically limited in the embodiments of the present invention. When the absolute value of the difference between the first roof elevation and the second roof elevation is less than the accuracy requirement for building heights in actual applications, the forward matching result and the reverse matching result are considered consistent. In this case, the average of the first and second roof elevations is calculated as the absolute roof elevation of the target building. Otherwise, the forward matching result and the reverse matching result are considered inconsistent, and the match is determined to have failed.

[0173] The embodiment of the present invention adopts a forward and reverse matching method, that is, first in the forward matching, the digital orthophoto is used as a reference and the remote sensing satellite image is used as the target to determine the forward value of the target building roof elevation, that is, the first roof elevation. Then, with the projection of the contour vector node under the first roof elevation on the remote sensing satellite image as a reference, reverse matching is performed on the digital orthophoto to obtain the reverse value of the absolute elevation of the building roof, that is, the second roof elevation. Only when the elevation difference obtained by the forward and reverse matching is less than the preset accuracy requirement, the forward and reverse matching results are considered consistent, that is, the results are acceptable, thereby effectively improving the accuracy and reliability of the matching.

[0174] See also Figure 9 , Figure 9 The present invention provides a structural block diagram of a device for extracting the height of a building. The device for extracting the height of a target building includes:

[0175] Matching and positioning module 1, used for matching and positioning the remote sensing satellite image of the target area according to the digital elevation model and digital orthophoto of the target area;

[0176] A bottom elevation acquisition module 2 is configured to acquire the bottom elevation of a target building in the target area using the digital elevation model;

[0177] The roof absolute elevation acquisition module 3 is used to align the vector outline of the target building in the digital line map with the digital orthophoto and the remote sensing satellite image after matching and positioning according to the bottom elevation, so as to obtain the absolute elevation of the roof of the target building;

[0178] The building height determination module 4 is used to determine the height of the target building according to the bottom elevation and the absolute roof elevation.

[0179] In an optional embodiment, the roof absolute elevation acquisition module 3 includes:

[0180] a forward matching unit, configured to perform forward matching on the vector outline of the target building in the digital line map and the digital orthophoto according to the bottom elevation, to obtain a first roof elevation of the target building;

[0181] a reverse matching unit, configured to reversely match the forward matching vector outline of the target building with the remote sensing satellite image after matching and positioning, based on the bottom elevation, to obtain a second roof elevation of the target building; wherein the forward matching vector outline is obtained by forward matching the vector outline of the target building in the digital line map with the digital orthophoto;

[0182] An absolute elevation determination unit is configured to determine the absolute elevation of the roof of the target building according to the first roof elevation and the second roof elevation.

[0183] In an optional embodiment, the forward matching unit includes:

[0184] A first template acquisition subunit is configured to fit the vector outline of the target building in the digital line map with the digital orthophoto to obtain a grayscale reference template and a mask template of the target building;

[0185] The second template acquisition subunit is used to perform a roof elevation search cycle according to the bottom elevation, and obtain an elevation sampling target template output in each cycle;

[0186] A correlation coefficient determination subunit, configured to determine a correlation coefficient between the grayscale reference template and the elevation sampling target template outputted in each cycle based on the grayscale reference template, the mask template, and the elevation sampling target template outputted in each cycle;

[0187] The first roof elevation calculation subunit is configured to determine the first roof elevation of the target building according to the correlation coefficient.

[0188] In an optional embodiment, the second template obtaining subunit includes:

[0189] The elevation search loop subunit is used to perform a roof elevation search loop with the bottom elevation as an initial value and in accordance with a set incremental step size;

[0190] The coordinate projection subunit is used to project the contour vector node of the target building corresponding to the elevation value searched in the current cycle onto the matched and positioned remote sensing satellite image in each roof elevation search cycle, and obtain the projection position of the corresponding contour vector node on the matched and positioned remote sensing satellite image;

[0191] The grayscale sampling subunit is used to sample the grayscale values ​​of the remote sensing satellite image after matching and positioning according to the projection position, and generate an elevation sampling target template corresponding to the elevation value searched in the current cycle.

[0192] In an optional embodiment, the first roof elevation calculation subunit includes:

[0193] A correlation coefficient comparison subunit is used to compare the correlation coefficient between the grayscale reference template and the elevation sampling target template output in each cycle;

[0194] The elevation value acquisition subunit is used to obtain the elevation value corresponding to the maximum correlation coefficient as the first roof elevation of the target building.

[0195] In an optional embodiment, the correlation coefficient determination subunit includes:

[0196] A first average value calculation subunit is used to calculate a first average value of pixel grayscales in the elevation sampling target template for the elevation sampling template output in each cycle;

[0197] A second average value calculation subunit, configured to calculate a second average value of pixel grayscales in the grayscale reference template;

[0198] The correlation coefficient calculation subunit is used to calculate the correlation coefficient between the grayscale reference template and the corresponding elevation sampling target template based on the first average value, the second average value, the grayscale values ​​of each pixel in the elevation sampling target template, the grayscale values ​​of each pixel in the grayscale reference template, and the mask values ​​in the mask template.

[0199] In an optional embodiment, the coordinate projection subunit includes:

[0200] A first coordinate transformation subunit is configured to transform the geographic coordinates of the contour vector node of the target building corresponding to the elevation value searched in the current cycle into the image coordinate system of the digital orthophoto to obtain the first transformed coordinates of the corresponding contour vector node;

[0201] A second coordinate transformation subunit is configured to translate the first transformation coordinates of each of the contour vector nodes to the local image coordinate system of the grayscale reference template to obtain the first image coordinates of each of the contour vector nodes on the grayscale reference template;

[0202] The reprojection subunit is used to reproject the first image coordinates of each of the contour vector nodes onto the remote sensing satellite image after matching and positioning according to the rational function model and orientation parameters of the remote sensing satellite image, so as to obtain the projection position of each of the contour vector nodes on the remote sensing satellite image after matching and positioning.

[0203] In an optional embodiment, the reprojection subunit includes:

[0204] An RPC projection coordinate solver unit is configured to solve the RPC projection coordinates of the first image coordinates on the remote sensing satellite image according to the rational function model of the remote sensing satellite image based on the first image coordinates;

[0205] A third coordinate transformation subunit is configured to transform the RPC projection coordinates into the image coordinate system of the remote sensing satellite image according to the orientation parameters of the remote sensing satellite image, so as to obtain a mapping relationship between the grayscale reference template and the remote sensing satellite image;

[0206] The fourth coordinate transformation subunit is used to transform the first image coordinates of each of the contour vector nodes to the matched and positioned remote sensing satellite image according to the mapping relationship, so as to obtain the projection position of each of the contour vector nodes on the matched and positioned remote sensing satellite image.

[0207] In an optional embodiment, the matching and positioning module 1 includes:

[0208] An image registration unit, configured to automatically register the remote sensing satellite image and the digital orthophoto image to obtain homonymous points and their spatial plane coordinates;

[0209] The image orientation unit is used to automatically orient the remote sensing satellite image by interpolating the spatial plane coordinates of the same-name points in the digital elevation model.

[0210] It should be noted that the working process of each module in the building height extraction device described in the embodiment of the present invention can refer to the working process of the building height extraction method described in the above embodiment, and the technical effect achieved is the same as that of the building height extraction method described in the above embodiment, which will not be repeated here.

[0211] See also Figure 10 , Figure 10 This is a block diagram of a device for extracting building heights according to an embodiment of the present invention. The device includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable by the processor 21. When the processor 21 executes the computer program, it implements the steps described in the aforementioned building height extraction method embodiments, such as steps S1 through S4.

[0212] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the target building height extraction device.

[0213] The target building height extraction device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of a building height extraction device and does not limit the device to the building height extraction device. The device may include more or fewer components than shown in the diagram, or may combine certain components or different components. For example, the target building height extraction device may further include input and output devices, network access devices, buses, and the like.

[0214] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor 21 is the control center of the target building height extraction device, and utilizes various interfaces and lines to connect various parts of the entire building height extraction device.

[0215] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements the various functions of the target building height extraction device by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0216] Wherein, if the module / unit integrated in the target building height extraction device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0217] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0218] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, many improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for extracting building height, characterized in that: include: Matching and positioning the remote sensing satellite image of the target area based on the digital elevation model and digital orthophoto of the target area; Obtaining the bottom elevation of a target building in the target area through the digital elevation model; According to the bottom elevation, the vector outline of the target building in the digital line map is aligned with the digital orthophoto and the remote sensing satellite image after matching and positioning to obtain the absolute elevation of the roof of the target building; The height of the target building is determined according to the bottom elevation and the absolute roof elevation.

2. The building height extraction method according to claim 1, wherein: According to the bottom elevation, registering the vector outline of the target building in the digital line map with the digital orthophoto and the matched and positioned remote sensing satellite image to obtain the absolute elevation of the roof of the target building includes: According to the bottom elevation, forward matching is performed between the vector outline of the target building in the digital line map and the digital orthophoto to obtain a first roof elevation of the target building; According to the bottom elevation, reverse matching is performed on the forward matching vector outline of the target building and the remote sensing satellite image after matching and positioning to obtain a second roof elevation of the target building; wherein the forward matching vector outline is obtained after forward matching is completed between the vector outline of the target building in the digital line map and the digital orthophoto; The absolute roof elevation of the target building is determined according to the first roof elevation and the second roof elevation.

3. The building height extraction method according to claim 2, wherein: The step of forward matching the vector outline of the target building in the digital line map with the digital orthophoto according to the bottom elevation to obtain a first roof elevation of the target building includes: Overlaying the vector outline of the target building in the digital line map with the digital orthophoto to obtain a grayscale reference template and a mask template of the target building; Performing a roof elevation search cycle according to the bottom elevation to obtain an elevation sampling target template outputted in each cycle; Determine the correlation coefficient between the grayscale reference template and the elevation sampling target template outputted in each cycle according to the grayscale reference template, the mask template and the elevation sampling target template outputted in each cycle; A first roof elevation of the target building is determined according to the correlation coefficient.

4. The method for extracting building height according to claim 3, wherein: The step of performing a roof elevation search cycle according to the bottom elevation to obtain an elevation sampling target template outputted in each cycle includes: Taking the bottom elevation as the initial value, a roof elevation search cycle is performed according to a set incremental step size; In each roof elevation search cycle, the contour vector node corresponding to the target building is projected onto the matched and positioned remote sensing satellite image according to the elevation value searched in the current cycle, and the projection position of the corresponding contour vector node on the matched and positioned remote sensing satellite image is obtained; According to the projection position, grayscale value sampling is performed on the remote sensing satellite image after matching and positioning, and an elevation sampling target template corresponding to the elevation value searched in the current cycle is generated.

5. The method for extracting building height according to claim 3, wherein: Determining the first roof elevation of the target building according to the correlation coefficient includes: Comparing the correlation coefficients between the grayscale reference template and the elevation sampling target template outputted in each cycle; The elevation value corresponding to the maximum correlation coefficient is obtained as the first roof elevation of the target building.

6. The method for extracting building height according to claim 3, wherein: The step of determining the correlation coefficient between the grayscale reference template and the elevation sampling target template outputted in each cycle according to the grayscale reference template, the mask template, and the elevation sampling target template outputted in each cycle includes: For the elevation sampling template output in each cycle, calculating the first average value of the pixel grayscale in the elevation sampling target template; Calculating a second average value of pixel grayscales in the grayscale reference template; The correlation coefficient between the grayscale reference template and the corresponding elevation sampling target template is calculated based on the first average value, the second average value, the grayscale values ​​of each pixel in the elevation sampling target template, the grayscale values ​​of each pixel in the grayscale reference template, and the mask values ​​in the mask template.

7. The method for extracting building height according to claim 4, wherein: The step of projecting the contour vector node of the target building corresponding to the elevation value found in the current cycle onto the matched and positioned remote sensing satellite image to obtain the projection position of the corresponding contour vector node on the matched and positioned remote sensing satellite image includes: Transforming the geographic coordinates of the contour vector node of the target building corresponding to the elevation value searched in the current cycle into the image coordinate system of the digital orthophoto to obtain the first transformed coordinates of the corresponding contour vector node; translating the first transformed coordinates of each of the contour vector nodes to the local image coordinate system of the grayscale reference template to obtain the first image coordinates of each of the contour vector nodes on the grayscale reference template; The first image coordinates of each of the contour vector nodes are reprojected onto the matched and positioned remote sensing satellite image according to the rational function model and orientation parameters of the remote sensing satellite image to obtain the projection position of each of the contour vector nodes on the matched and positioned remote sensing satellite image.

8. The method for extracting building height according to claim 7, wherein: The step of reprojecting the first image coordinates of each of the contour vector nodes onto the matched and positioned remote sensing satellite image according to the rational function model and orientation parameters of the remote sensing satellite image to obtain the projection position of each of the contour vector nodes on the matched and positioned remote sensing satellite image includes: Calculating RPC projection coordinates of the first image coordinates on the remote sensing satellite image according to the rational function model of the remote sensing satellite image according to the first image coordinates; transforming the RPC projection coordinates into the image coordinate system of the remote sensing satellite image according to the orientation parameters of the remote sensing satellite image, to obtain a mapping relationship between the grayscale reference template and the remote sensing satellite image; According to the mapping relationship, the first image coordinates of each of the outline vector nodes are transformed to the matched and positioned remote sensing satellite image to obtain the projection position of each of the outline vector nodes on the matched and positioned remote sensing satellite image.

9. The method for extracting building height according to claim 1, wherein: The matching and positioning of the remote sensing satellite image of the target area according to the digital elevation model and the digital orthophoto of the target area includes: Automatically registering the remote sensing satellite image and the digital orthophoto image to obtain homonymous points and their spatial plane coordinates; The remote sensing satellite image is automatically oriented by interpolating the spatial plane coordinates of the points of the same name in the digital elevation model.

10. A building height extraction device, characterized in that: include: A matching and positioning module is used to match and position the remote sensing satellite image of the target area based on the digital elevation model and digital orthophoto of the target area; A bottom elevation acquisition module, configured to acquire the bottom elevation of a target building in the target area using the digital elevation model; A roof absolute elevation acquisition module is used to align the vector outline of the target building in the digital line map with the digital orthophoto and the matched and positioned remote sensing satellite image based on the bottom elevation to obtain the absolute elevation of the roof of the target building; The building height determination module is used to determine the height of the target building according to the bottom elevation and the absolute roof elevation.

11. A building height extraction device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for extracting the building height according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the building height extraction method according to any one of claims 1 to 9.

13. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the building height extraction method according to any one of claims 1 to 9 is implemented.

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