SAR image positioning method and system based on laser point cloud and optical image

Abstract

The present invention provides a SAR image positioning method and system based on laser point clouds and optical images. The method includes: automatically matching the connection points of the collected optical image and SAR image to obtain the same-name image points; using the same-name image points in the optical image and the elevation data of the same-name image points in the laser point cloud, performing a joint regional block adjustment to obtain a first affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the laser point cloud; using the three-dimensional coordinates of the same-name image points on the laser point cloud, performing a free network adjustment on the optical image to obtain a second affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the optical image; and using the three-dimensional coordinates of the same-name image points on the optical image, performing a control regional block adjustment on the SAR image to obtain the three-dimensional coordinates of the same-name image points on the SAR image and accurate SAR image affine transformation coefficients. The method of the present invention takes into account multi-source data and improves the overall positioning accuracy of the SAR image through joint positioning.

Description

Technical Field

[0001] The present invention belongs to the field of surveying and mapping technology, and relates to a SAR image positioning method and system based on laser point cloud and optical image. Background Art

[0002] my country's remote sensing and mapping satellites have achieved global observation, providing a large number of high-quality surveying and mapping products for the Belt and Road Initiative. However, due to the lack of access to high-precision ground control data in some areas, further improvement in stereo measurement accuracy is difficult. The emergence of products such as laser altimetry data, optical stereo satellite imagery, and SAR satellite imagery has made high-precision stereo measurement of terrain possible. In fields such as geographic information systems (GIS), civil engineering, and surveying, adjustment methods are commonly used to address errors in various measurement and positioning processes, improving the accuracy and reliability of measurement data and ensuring high positioning precision.

[0003] Adjustment is a mathematical method used to process measurement data and adjust measurement results. In practical applications, it can effectively handle a variety of complex measurement data, addressing problems such as measurement errors, inconsistent observation accuracy, and incomplete data, thereby improving the reliability and accuracy of measurement results. It provides important basic data support for engineering design, cartography, and spatial data analysis. Among them, joint adjustment is a commonly used adjustment method that can comprehensively process multiple types of observation data and simultaneously optimize multiple measurement parameters. Specifically, joint adjustment can uniformly adjust different types of observation data (such as distance, angle, elevation, etc.) and data from different measurement tasks or equipment, thereby obtaining more accurate and consistent measurement results. Its basic principle is to establish a mathematical model that takes into account various observation data and the relationships between them. Then, this comprehensive model is adjusted using the least squares method or other mathematical optimization techniques to ensure that the individual observation data can achieve optimal consistency under unified parameters.

[0004] During joint adjustment, laser altimetry data, optical stereo satellite imagery, SAR satellite imagery, and other measurement data have significant differences in initial positioning accuracy, imaging mechanisms, and processing methods, which affects positioning accuracy. For example, laser altimetry data can be used as a control benchmark to improve the overall positioning accuracy of multi-source data. However, because satellite-borne lasers are not equipped with footprint cameras, the specific locations of laser altimeter points cannot be determined, making it difficult to achieve consistency in object-image coordinates. This makes the optimization strategy for joint adjustment of laser altimetry data a difficult point in multi-source data joint positioning. Therefore, how to achieve joint adjustment and positioning solutions for multi-source heterogeneous data is the key to improving multi-source data positioning accuracy. Summary of the Invention

[0005] In order to effectively improve the positioning accuracy of SAR images, the present invention discloses a SAR image positioning method based on laser point cloud and optical image. This method fully considers the use of multi-source data such as optical images, satellite-borne laser point cloud data, open source DEM, etc., combines the high positioning accuracy of optical images themselves (such as the Tianhui-3 satellite) and the fact that satellite-borne laser radar data can be used as control points, and improves the overall positioning accuracy of SAR images through joint positioning. Specifically, the SAR image positioning method includes the following steps:

[0006] S1. Automatically match the connection points of the collected optical image and SAR image to obtain image points with the same name;

[0007] S2, using the image points with the same name in the optical image and the elevation data of the image points with the same name in the collected laser point cloud, performing a joint block adjustment to obtain first affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the laser point cloud;

[0008] S3, performing free network adjustment on the optical image by using the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud, to obtain a second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image;

[0009] S4. Performing a control area block adjustment on the SAR image using the second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the image points with the same name on the SAR image.

[0010] Furthermore, in the above step S1, the automatic matching of connection points of the collected optical image and SAR image to obtain image points with the same name includes:

[0011] S11, establishing an image pyramid using the collected optical image as a reference image;

[0012] S12, performing feature extraction and logical block division on the bottom layer of the image pyramid using a Fonstner operator to obtain a plurality of logical blocks, and mapping the plurality of logical blocks to other layers of the image pyramid, wherein each of the logical blocks includes a plurality of feature points;

[0013] S13, calculating initial radiometric transformation parameters from the optical image to the SAR image based on the corner point coordinates and geographic coordinate mapping at the bottom layer of the image pyramid;

[0014] S14. Automatically matching connection points using the initial radiation transformation parameters and the logic block using a correlation coefficient method and a least squares method to obtain a plurality of image points with the same name.

[0015] Furthermore, in the above step S2, the use of the same-name image points in the optical image and the elevation data of the same-name image points in the collected laser point cloud to perform a joint block adjustment to obtain first affine transformation coefficients and three-dimensional coordinates of the same-name image points on the laser point cloud includes:

[0016] S21, performing block adjustment on the optical image by a rational function algorithm to obtain a first initial affine transformation coefficient;

[0017] S22. Iterate the first initial affine transformation coefficient by joint adjustment of the optical image and the laser point cloud to obtain the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud.

[0018] Furthermore, in the above step S22, the first initial affine transformation coefficient is iterated by the joint adjustment of the optical image and the laser point cloud to obtain the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud, including:

[0019] S221, performing connection point matching on the image points with the same name in the optical image and the laser point cloud to obtain laser height measurement points corresponding to the image points with the same name, and back-projecting the laser height measurement points onto the optical image to obtain back-projected image points;

[0020] S222, selecting a back-projected image point at a forward-looking position or a downward-looking position as a reference point, and performing matching adjustment on the back-projected image points corresponding to the image point with the same name;

[0021] S223 , by matching the adjusted object-space coordinates of the laser height measurement point with the corresponding image-space coordinates, iteratively obtain the first initial affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud.

[0022] Furthermore, in the above step S223, the first initial affine transformation coefficient is iterated to obtain the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud by matching the adjusted object space coordinates of the laser height measurement point with the corresponding image space coordinates, including:

[0023] S2231, the object space initial coordinate P of the image point with the same name corresponding to the laser height measurement point I 9X1, Y1, Z1) and the image coordinates P(X2, Y2, Z20) of the laser altimeter point as control points;

[0024] S2232, respectively assign a weight to the error equations of the object space initial coordinates and the image space coordinates of the laser altimeter point, take the elevation value of the image space coordinates as the true value, and perform a correction on the first initial affine transformation coefficient, the object space coordinates of the image point with the same name, and the plane coordinates of the laser altimeter point. Perform iterative calculation until the plane coordinate correction value When it is less than a set threshold, the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud are obtained.

[0025] Furthermore, in the above step S3, the free network adjustment of the optical image is performed using the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud to obtain the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image, including:

[0026] S31, establishing a first mapping relationship between the image-space coordinates of the optical image and the corresponding object-space coordinates through a rational function algorithm;

[0027] S32, when systematic error compensation polynomial is taken once, according to described first mapping relation, by setting up the first error equation of block adjustment based on RFM based on the image space affine transformation model of image space compensation;

[0028] S33. Solve the first error equation to obtain second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image.

[0029] Furthermore, in the above step S32, the expression of the image-space affine transformation model based on image-space compensation is: Where (a0, a1, a2) and (b0, b1, b2) are the affine transformation parameters of the image space in the optical image, s and l are the image space coordinates and the corresponding object space coordinates of the image points with the same name in the optical image, respectively, and Δs and Δl are the correction vectors of s and l, respectively.

[0030] Furthermore, in the above step S4, performing a control block adjustment on the SAR image using the second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image to obtain the third radiometric transformation coefficients and the three-dimensional coordinates of the image points with the same name on the SAR image includes:

[0031] S41, using the three-dimensional coordinates of the image points with the same name on the optical image as control points, and establishing a second mapping relationship between the image-space coordinates and the corresponding object-space coordinates in the SAR image through a rational function algorithm;

[0032] S42, when systematic error compensation polynomial is got once, according to described second mapping relation, by setting up the second error equation of block adjustment based on RFM based on the image space affine transformation model of image space compensation;

[0033] S43. Taking the coordinate values ​​of the control points as true values, solving the second error equation to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the image points with the same name on the SAR image.

[0034] Furthermore, in the above step S42, the expression of the image-space affine transformation model based on image-space compensation is: Where (a0, a1, a2) and (b0, b1, b2) are the affine transformation parameters of the image space in the SAR image, s and l are the image space coordinates and the corresponding object space coordinates of the same-name image points in the SAR image, respectively, and Δs and Δl are the correction vectors of s and l, respectively.

[0035] The embodiment of the present invention further provides a SAR image positioning system based on laser point cloud and optical image, which includes an automatic matching module, a regional block adjustment module, a free network adjustment module and a control regional block adjustment module.

[0036] The automatic matching module is used to automatically match the connection points of the collected optical image and SAR image to obtain image points with the same name;

[0037] The block adjustment module is used to use the same-name image points in the optical image and the elevation data of the same-name image points in the collected laser point cloud to perform a joint block adjustment to obtain first affine transformation coefficients and three-dimensional coordinates of the same-name image points on the laser point cloud;

[0038] The free network adjustment module is used to perform free network adjustment on the optical image by using the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud to obtain the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image;

[0039] The control area block adjustment module is used to perform control area block adjustment on the SAR image using the second affine transformation coefficients and the three-dimensional coordinates of the same-name image points on the optical image to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the same-name image points on the SAR image.

[0040] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the computer device implements any of the above-mentioned SAR image positioning methods based on laser point clouds and optical images, thereby effectively improving the positioning accuracy of SAR images.

[0041] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program for executing any of the above-mentioned SAR image positioning methods based on laser point clouds and optical images, so as to effectively improve the positioning accuracy of SAR images.

[0042] Compared with the prior art, the beneficial effects achieved by at least one of the above technical solutions adopted in the embodiments of this specification include at least the following: the SAR image positioning method of the present invention first automatically matches the connection points of the optical image and the SAR image to obtain multiple same-name image points; secondly, a joint regional block adjustment is performed using the coordinates of the same-name image points in the optical image and their elevation data in the laser point cloud to obtain a first affine transformation coefficient and the three-dimensional coordinates of each same-name image point in the laser point cloud; thirdly, a free network adjustment is performed on the optical image using the first affine transformation coefficient and the three-dimensional coordinates of the same-name image points in the laser point cloud to obtain a second affine transformation coefficient and the three-dimensional coordinates of the same-name image points in the optical image; finally, a control regional block adjustment is performed on the SAR image using the second affine transformation coefficient and the three-dimensional coordinates of the same-name image points in the optical image to obtain a third radiometric transformation coefficient and the three-dimensional coordinates of each same-name image point in the SAR image. The method of the present invention improves the overall positioning accuracy of the SAR image by using the high-precision, high-resolution optical image and the altimetry data of the point cloud data as control reference data through a joint positioning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 This is a flow chart of the SAR image positioning method based on laser point cloud and optical image disclosed in an embodiment of the present invention;

[0045] Figure 2 A technical roadmap for the SAR image positioning method based on laser point cloud and optical imagery disclosed in an embodiment of the present invention;

[0046] Figure 3 This is an architecture diagram of a SAR image positioning system based on laser point cloud and optical imagery disclosed in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of a computer device disclosed in an embodiment of the present invention;

[0048] Among them, 301 is an automatic matching module; 302 is a regional network adjustment module; 303 is a free network adjustment module; 304 is a control regional network adjustment module; 401 is a memory; and 402 is a processor. DETAILED DESCRIPTION

[0049] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0050] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features of the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0051] Laser point clouds, also known as laser altimetry data, are large amounts of discrete point data acquired by a laser radar (LiDAR) system, used to describe the three-dimensional spatial information of the surface of an object. By emitting laser pulses and recording the return time and intensity of these pulses, the position and shape of the target surface can be accurately measured, generating high-density three-dimensional point cloud data. Laser altimetry (LiDAR) is a method for acquiring surface elevation information using laser technology. By emitting laser pulses and recording their reflection time, elevation measurement and three-dimensional modeling of the surface can be achieved.

[0052] Optical imagery, also known as optical stereo satellite imagery, is image data of the Earth's surface acquired using optical cameras or optical remote sensors, typically including visible light images and near-infrared band images. These image data digitally record the surface features and object information of the Earth's surface and can be used in a variety of fields, including map production, land use classification, environmental monitoring, and urban planning. SAR imagery, Synthetic Aperture Radar (SAR) is an active remote sensing technology that can acquire image data of the Earth's surface by emitting microwave signals and recording their echoes. SAR imagery has many unique characteristics, making it widely applicable in Earth observation and environmental monitoring.

[0053] Object-image coordinates usually refer to the two coordinate systems used in optical imaging: object-space coordinates and image-space coordinates. Object-space coordinates refer to the coordinate position of a point on the object being photographed on the actual object. In geographic information systems (GIS) or remote sensing, object-space coordinates can be actual geographic coordinates on the Earth's surface, such as longitude and latitude. Image-space coordinates refer to the coordinate position of the image formed by the object during the imaging process on the imaging plane. In remote sensing image processing, image-space coordinates are usually pixel row and column coordinates, used to represent the position on the digital image.

[0054] An embodiment of the present invention discloses a SAR image positioning method based on laser point clouds and optical images. The method uses laser altimetry data, optical stereo satellite images, and SAR satellite image data for joint adjustment. The method fully considers the use of multiple source data such as existing optical stereo satellite images, spaceborne laser altimetry data, and open source DEMs, and combines the high positioning accuracy of optical stereo images themselves (such as the Tianhui-3 satellite) and the fact that spaceborne lidar data can be used as control points to improve the positioning accuracy of SAR images.

[0055] The present invention describes in detail the SAR image positioning method based on laser point cloud and optical image through the following embodiments. Figure 1 and Figure 2 As shown, the combined adjustment method includes the following steps:

[0056] S1. Automatically match the connection points of the collected optical image and SAR image to obtain image points with the same name;

[0057] S2, using the image points with the same name in the optical image and the elevation data of the image points with the same name in the collected laser point cloud, performing a joint block adjustment to obtain first affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the laser point cloud;

[0058] S3, performing free network adjustment on the optical image by using the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud, to obtain a second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image;

[0059] S4. Performing a control area block adjustment on the SAR image using the second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the image points with the same name on the SAR image.

[0060] When the above step S1 is implemented, the automatic matching of connection points of the collected optical image and SAR image to obtain image points with the same name includes:

[0061] S11. Establish an image pyramid using the collected optical image as a reference image, wherein the size of the top layer of the established image pyramid is not less than 512×512.

[0062] S12. Perform feature extraction on the bottom layer of the image pyramid using a Fonstner operator to ensure that the distribution of feature points is as uniform as possible. Then, perform logical segmentation to obtain a plurality of logical blocks, such that each logical block has a substantially uniform number of feature points. The plurality of logical blocks are respectively mapped to other layers in the image pyramid.

[0063] S13. Calculate initial radiometric transformation parameters from the optical image to the SAR image based on the coordinates of the corner points in the bottom layer of the image pyramid and the geographic coordinate mapping, where the initial radiometric transformation parameters can be expressed as a0, a1, a2, b0, b1, and b2.

[0064] S14. Automatically matching connection points using the initial radiation transformation parameters and the logic block using a correlation coefficient method and a least squares method to obtain a plurality of image points with the same name.

[0065] The above step S14, when implemented, includes the following process:

[0066] S141. At the top level of the image pyramid, based on the obtained six initial affine transformation parameters (a0, a1, a2, b0, b1, b2), a matching window corresponding to a reference window (corresponding to a logical block) of the SAR image and the optical image is calculated, and image resampling is performed on the matching window based on the reference window.

[0067] S142. For the target window at the top level (corresponding to a logical block), extract and map the feature points (e.g., m in total) of the SAR image according to the above-mentioned Fonstner operator, calculate the conjugate image points of each feature point in the resampled search window by image matching using the correlation coefficient method, retain the conjugate image points (e.g., k in total, k<m) with the largest correlation coefficient and greater than the threshold, and then use the Ransac idea to eliminate the mismatched points to obtain the final conjugate image points (e.g., l in total, l<k).

[0068] S143. Use the above-mentioned matched l conjugate image points to calculate the affine transformation parameters (6 in total) between the top-level target window and the search window. Use this set of affine transformation parameters to calculate the quasi-conjugate image points of other feature points (ml) in the top-level target window. The l conjugate image points successfully matched in step S142 are retained together.

[0069] S144: The matching results from step S143 are transferred to the next level of the image pyramid. This transfer only needs to be performed on the search image. Then, steps S141 to S143 are returned to perform the same matching process on the top level of the image pyramid. Note that the image must be resampled each time using the affine transformation parameters updated in step S143. Step S144 is repeated until the matching results are transferred to the bottom level of the image pyramid.

[0070] S145, at the bottom layer of the image pyramid, return to steps S141 to S142 to obtain reliable conjugate points of the target window, and on this basis, perform least square matching to improve the accuracy of image matching. Finally, use the affine transformation formula Calculate the corresponding image points with the same name on the search image, where (x, y) and (X, Y) are the coordinates of the corresponding image points with the same name on the SAR image and the optical image.

[0071] S146 , repeat S141 to S145 in the same manner to complete the matching of other target windows. The matching of each target window can be performed using a parallel matching process.

[0072] When the above step S2 is implemented, the use of the same-name image points in the optical image and the elevation data of the same-name image points in the collected laser point cloud to perform a joint block adjustment to obtain first affine transformation coefficients and the three-dimensional coordinates of the same-name image points on the laser point cloud includes:

[0073] S21, performing block adjustment on the optical image by a rational function algorithm to obtain a first initial affine transformation coefficient;

[0074] S22. Performing a joint block adjustment on the optical image and the elevation data in the laser point cloud, iterating the first initial affine transformation coefficient to obtain a first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud.

[0075] The rational function algorithm used in step S21 is also known as the Rational Function Model (RFM). This is a universal sensor model applicable to various sensors and is a commonly used mathematical model for high-resolution optical satellite sensors. The RFM uses the object-space 3D coordinates of the optical image as the independent variable and the image-space coordinates of the optical image as the dependent variable. Its expression is: In the expression, (c n , r n 0 is the normalized image coordinate of the optical image, (X n , Y n , Z n ) is the normalized object coordinate of the optical image, are polynomial coefficients. The above normalized coordinates can be expressed as: Among them, (c, r) and (X, Y, Z) are the image space coordinates and object space coordinates respectively, c0, r0, X0, Y0, Z0 are translation parameters, c s , r s , X s , Y s , Z s is the normalization parameter.

[0076] Among them, the RFM compensation model can be expressed as

[0077] Where (Δc, Δr) is the compensation value of the image space coordinate:

[0078] Δc=a0+a1c+a2r,Δr=b0+b1c+b2r,where a i , b i (i=0, 1, 2)

[0079] is the first initial affine transformation coefficient.

[0080] Among them, the regional block adjustment error equation based on the RFM compensation equation can be expressed as:

[0081]

[0082] Among them, the matrix form of the above formula can be simplified to: V ij =A ij t j +B ij x i -l ij ;P ij , where V ij is the residual vector of the RFM compensation equation, A ij and B ij is the observation matrix of the RFM compensation equation, t j is the affine transformation coefficient correction vector, x i is the object space coordinate correction vector of the connection point, l ij is a constant vector, P ij is the weight matrix (the control point weights are often set several orders of magnitude higher than the unit weight).

[0083] When the above step S22 is implemented, the optical image and the elevation data in the laser point cloud are jointly block adjusted to iterate the first initial affine transformation coefficient to obtain the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud, including:

[0084] S221, performing connection point matching on the image points with the same name in the optical image and the laser point cloud to obtain laser height measurement points corresponding to the image points with the same name, and back-projecting the laser height measurement points onto the optical image to obtain back-projected image points;

[0085] S222, selecting a back-projected image point at a forward-looking position or a downward-looking position as a reference point, and performing matching adjustment on the back-projected image points corresponding to the image point with the same name;

[0086] S223 , by matching the adjusted object-space coordinates of the laser height measurement point with the corresponding image-space coordinates, iteratively obtain the first initial affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud.

[0087] When the above step S223 is implemented, the first initial affine transformation coefficient is iterated to obtain the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud by matching the adjusted object space coordinates of the laser height measurement point with the corresponding image space coordinates, including:

[0088] S2231, the object space initial coordinate P of the image point with the same name corresponding to the laser height measurement point I (X1, Y1, Z1) and the image coordinates P (X2, Y2, Z2) of the laser altimeter point as control points;

[0089] S2232, respectively assign a weight to the error equations of the object space initial coordinates and the image space coordinates of the laser altimeter point, take the elevation value of the image space coordinates as the true value, and perform a correction on the first initial affine transformation coefficient, the object space coordinates of the image point with the same name, and the plane coordinates of the laser altimeter point. Perform iterative calculation until the plane coordinate correction value When it is less than a set threshold, the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud are obtained.

[0090] In specific implementation, the weight of the error equation of the object space initial coordinates can be assigned to be 1, and the weight of the error equation of the image space coordinates of the laser height measurement point can be assigned to be 100. The elevation value Z2 of the image space coordinates is taken as the true value, which can be expressed as: Solve the above error equation, and then iteratively calculate the first initial affine transformation coefficient, the object coordinates of the connection point, and the plane coordinates of point P Correction value, until the correction value of the plane coordinate of the laser height measurement point P is less than the limit error, then the adjustment result is output, wherein the adjustment result includes the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud.

[0091] Through steps S21 and S22, the coordinates of the x-direction and y-direction in the plane coordinates of the laser height measurement point can be continuously iterated, so that the object coordinates can be closer to the true value, thereby improving the problem of inconsistency of object image coordinates in the laser point cloud, and can effectively improve the plane accuracy of the laser height measurement point, thereby improving the overall joint adjustment accuracy of optical images and SAR images.

[0092] When the above step S3 is implemented, the optical image is subjected to free network adjustment by using the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud to obtain the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image, including:

[0093] S31. Establish a first mapping relationship between the image-space coordinates of the optical image and the corresponding object-space coordinates through a rational function algorithm, wherein the first mapping relationship can be expressed as: Where s and l are the coordinates of the image point, and the power of each coordinate component of each term in the polynomial and the sum of their powers are no more than 3.

[0094] S32. When the system error compensation polynomial is taken once, according to the first mapping relationship, a first error equation of the RFM-based block adjustment is established through an image space affine transformation model based on image space compensation.

[0095] Among them, the expression of the image-space affine transformation model based on image-space compensation is: Where (a0, a1, a2) and (b0, b1, b2) are the affine transformation parameters of the image space in the optical image, s and l are the image space coordinates and the corresponding object space coordinates of the image points with the same name in the optical image, respectively, and Δs and Δl are the correction vectors of s and l, respectively.

[0096] The first error equation can be expressed as: V = AX + BY - L, P, where V represents the residual vector of the row and column coordinate observations of the image point; X is the correction vector of the image coordinate system error compensation parameters (i.e., the six affine transformation parameters); Y is the correction vector of the ground geodetic coordinates corresponding to the connection point; A is the coefficient matrix of the unknown number X; B is the coefficient matrix of the unknown number Y; L is the constant term obtained from the initial value calculation; and P is the weight matrix. Here, X = [Δa0Δa1Δa2Δb0Δb1Δb2] T , Y=[ΔLat,ΔLon,ΔHeight] T , V=[v x v y ] T .

[0097] S33. Solve the first error equation to obtain second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image.

[0098] When the above steps S31 to S33 are implemented, for the control points in step S2231, what needs to be corrected in the above first error equation are the affine transformation parameters in the optical image, and for the image points with the same name, the first error equation needs to correct the affine transformation parameters in the optical image and their corresponding object-space geodetic coordinates at the same time, that is, to obtain the second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image.

[0099] When the above step S4 is implemented, the control area block adjustment is performed on the SAR image using the second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image to obtain the third radiometric transformation coefficients and the three-dimensional coordinates of the image points with the same name on the SAR image, including:

[0100] S41. Using the three-dimensional coordinates of the image points with the same name on the optical image as control points, a second mapping relationship between the image-space coordinates and the corresponding object-space coordinates in the SAR image is established using a rational function algorithm. The second mapping relationship is the same as the first mapping relationship and can be expressed as: Where s and l are the coordinates of the image point, and the power of each coordinate component of each term in the polynomial and the sum of their powers are no more than 3.

[0101] S42. When the system error compensation polynomial is taken once, according to the second mapping relationship, a second error equation of the RFM-based block adjustment is established by using an image space affine transformation model based on image space compensation.

[0102] Among them, the expression of the image-space affine transformation model based on image-space compensation is: Where (a0, a1, a2) and (b0, b1, b2) are the affine transformation parameters of the image space in the optical image, s and l are the image space coordinates and the corresponding object space coordinates of the image points with the same name in the optical image, respectively, and Δs and Δl are the correction vectors of s and l, respectively.

[0103] The second error equation is the same as the first and can be expressed as: V = AX + BY - L, P, where V represents the residual vector of the row and column coordinate observations of the image point; X is the correction vector of the image coordinate system error compensation parameters (i.e., the six affine transformation parameters); Y is the correction vector of the ground geodetic coordinates corresponding to the connection point; A is the coefficient matrix of the unknown X; B is the coefficient matrix of the unknown Y; L is the constant term obtained from the initial value calculation; and P is the weight matrix. Here, X = [Δa0 Δa1 Δa2 Δb0 Δb1Δb2] T , Y=[ΔLat,ΔLon,ΔHeight] T , V=[v x vy ] T .

[0104] S43. Taking the coordinate values ​​of the control points as true values, solving the second error equation to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the image points with the same name on the SAR image.

[0105] At this time, the affine term parameters of the image square compensation are taken as unknowns and solved together with the unknowns such as the plane coordinates of the ground points, and the new regional block adjustment error equation based on the RFM model is obtained, which can be expressed as: Among them, ΔX and ΔY are the ground coordinate correction values ​​of the point to be determined, that is, the three-dimensional coordinates of the image point with the same name on the SAR image are obtained.

[0106] It should be noted that the controlled area block adjustment model of step S4 adopts the RFM model identical with the control area block adjustment in step S3, but for the controlled area block adjustment model, the coordinate value of control point in its error equation is true value, when carrying out the transformation parameter solution, there is no need to carry out parameter solution using control point coordinate as unknown number.

[0107] Based on the same inventive concept, an embodiment of the present invention further provides a joint adjustment system of laser point cloud, optical image and SAR image, as described in the following embodiments. Since the principle of solving the problem by the SAR image positioning system based on laser point cloud and optical image is similar to the SAR image positioning method based on laser point cloud and optical image in the above embodiment, the implementation of the joint adjustment system can refer to the implementation of the above-mentioned joint adjustment method, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0108] Figure 3 This is a structural block diagram of a SAR image positioning system based on laser point cloud and optical image disclosed in an embodiment of the present invention, such as Figure 3 As shown, SAR image positioning includes an automatic matching module 301, a regional block adjustment module 302, a free network adjustment module 303 and a control region block adjustment module 304. The structure is described below.

[0109] The automatic matching module 301 is used to automatically match the connection points of the collected optical image and the SAR image to obtain image points with the same name;

[0110] The block adjustment module 302 is used to use the same-name image points in the optical image and the elevation data of the same-name image points in the collected laser point cloud to perform a joint block adjustment to obtain first affine transformation coefficients and three-dimensional coordinates of the same-name image points on the laser point cloud;

[0111] The free network adjustment module 303 is used to perform free network adjustment on the optical image by using the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud to obtain the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image;

[0112] The control area block adjustment module 304 is used to perform control area block adjustment on the SAR image using the second affine transformation coefficients and the three-dimensional coordinates of the same-name image points on the optical image to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the same-name image points on the SAR image.

[0113] The embodiments of the present invention achieve the following technical effects: The SAR image positioning method of the present invention first automatically matches the connection points of the optical image and the SAR image to obtain multiple image points with the same name; secondly, a joint regional block adjustment is performed based on the coordinates of the image points with the same name in the optical image and their elevation data in the laser point cloud to obtain a first affine transformation coefficient and the three-dimensional coordinates of each image point with the same name on the laser point cloud; thirdly, a free network adjustment is performed on the optical image based on the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud to obtain a second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image; finally, a control regional block adjustment is performed on the SAR image based on the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image to obtain a third radiometric transformation coefficient and the three-dimensional coordinates of each image point with the same name on the SAR image. The method of the present invention improves the overall positioning accuracy of the SAR image by using the high-precision, high-resolution optical image and the altimetry data of the point cloud data as control reference data through a joint positioning method.

[0114] In this embodiment, a computer device is provided, such as Figure 4 As shown, it includes a memory 401, a processor 402 and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned SAR image positioning methods based on laser point cloud and optical image is implemented.

[0115] Specifically, the computer device may be a computer terminal, a server or a similar computing device.

[0116] In this embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program for executing any of the above-mentioned SAR image positioning methods based on laser point clouds and optical images.

[0117] Specifically, computer-readable storage media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable storage media does not include transitory media such as modulated data signals and carrier waves.

[0118] Obviously, those skilled in the art should understand that the various modules or steps of the above-mentioned embodiments of the present invention can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices. Alternatively, they can be implemented using program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than herein, or they can be made into separate integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0119] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A SAR image positioning method based on laser point cloud and optical image, characterized in that: include: Automatically match the connection points of the collected optical image and SAR image to obtain the image points with the same name; Using the same-name image points in the optical image and the elevation data of the same-name image points in the collected laser point cloud, a joint block adjustment is performed to obtain first affine transformation coefficients and three-dimensional coordinates of the same-name image points on the laser point cloud, including: performing a block adjustment on the optical image using a rational function algorithm to obtain first initial affine transformation coefficients; performing a joint block adjustment on the optical image and the elevation data in the laser point cloud, iterating the first initial affine transformation coefficients to obtain first affine transformation coefficients and three-dimensional coordinates of the same-name image points on the laser point cloud; The optical image is subjected to free network adjustment using the first affine transformation coefficient and the three-dimensional coordinates of the image points of the same name on the laser point cloud to obtain second affine transformation coefficients and the three-dimensional coordinates of the image points of the same name on the optical image, including: establishing a first mapping relationship between the image space coordinates of the optical image and the corresponding object space coordinates using a rational function algorithm; establishing a first error equation for regional block adjustment based on RFM using an image space affine transformation model based on image space compensation according to the first mapping relationship when a system error compensation polynomial is taken as one; and solving the first error equation to obtain the second affine transformation coefficients and the three-dimensional coordinates of the image points of the same name on the optical image; The SAR image is subjected to a control block adjustment using the second affine transformation coefficients and the three-dimensional coordinates of the image points of the same name on the optical image to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the image points of the same name on the SAR image, including: using the three-dimensional coordinates of the image points of the same name on the optical image as control points, and establishing a second mapping relationship between image-space coordinates and corresponding object-space coordinates in the SAR image using a rational function algorithm; when a system error compensation polynomial is taken as one, establishing a second error equation for block adjustment based on RFM using an image-space affine transformation model based on image-space compensation according to the second mapping relationship; and using the coordinate values ​​of the control points as true values ​​to solve the second error equation to obtain the third radiometric transformation coefficients and the three-dimensional coordinates of the image points of the same name on the SAR image.

2. The SAR image positioning method based on laser point cloud and optical image according to claim 1, characterized in that: The automatic matching of connection points of the collected optical image and SAR image to obtain image points with the same name includes: An image pyramid is established using the collected optical image as a reference image; Performing feature extraction and logical block division on the bottom layer of the image pyramid using a Fonstner operator to obtain a plurality of logical blocks, and mapping the plurality of logical blocks to other layers of the image pyramid, wherein each of the logical blocks includes a plurality of feature points; Calculating initial radiometric transformation parameters from the optical image to the SAR image based on the coordinates of the corner points in the bottom layer of the image pyramid and the geographic coordinate mapping; The initial radiation transformation parameters and the logic block are used to automatically match the connection points using the correlation coefficient method and the least square method to obtain a plurality of image points with the same name.

3. The SAR image positioning method based on laser point cloud and optical image according to claim 1, characterized in that: The step of performing a joint block adjustment on the optical image and the elevation data in the laser point cloud, iterating the first initial affine transformation coefficient, and obtaining the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud, comprises: Connecting the image points with the same name in the optical image and the laser point cloud to obtain laser height measurement points corresponding to the image points with the same name, and back-projecting the laser height measurement points onto the optical image to obtain back-projected image points; Selecting a back-projected image point at a forward-looking position or a downward-looking position as a reference point, and performing matching adjustments on the back-projected image points corresponding to the image point with the same name; By matching the adjusted object space coordinates of the laser height measurement point with the corresponding image space coordinates, the first initial affine transformation coefficient is iterated to obtain the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud.

4. The SAR image positioning method based on laser point cloud and optical image according to claim 3, characterized in that: The step of iteratively obtaining the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud by matching the adjusted object-space coordinates of the laser height measurement point with the corresponding image-space coordinates comprises: Taking the object space initial coordinates of the image point with the same name corresponding to the laser altimeter point and the image space coordinates of the laser altimeter point as control points; A weight is assigned to the error equations of the object space initial coordinates and the image space coordinates of the laser altimeter point respectively, and the elevation value of the image space coordinates is taken as the true value. The first initial affine transformation coefficient, the object space coordinates of the image point with the same name, and the plane coordinate correction value of the laser altimeter point are iteratively calculated until the plane coordinate correction value is less than a set threshold, thereby obtaining the first affine transformation coefficient and the three-dimensional coordinates of the image point with the same name on the laser point cloud.

5. The SAR image positioning method based on laser point cloud and optical image according to claim 1, characterized in that: The expression of the image space affine transformation model based on image space compensation is: ,in, 、 is the affine transformation parameter of the image space in the optical image, s and l are the image space coordinates and the corresponding object space coordinates of the image points with the same name in the optical image, Δs and Δl are the correction vectors of s and l, respectively.

6. The SAR image positioning method based on laser point cloud and optical image according to claim 1, characterized in that: The expression of the image space affine transformation model based on image space compensation is: ,in, 、 is the affine transformation parameter of the image space in the SAR image, s and l are the image space coordinates and the corresponding object space coordinates of the same-name image points in the SAR image, Δs and Δl are the correction vectors of s and l, respectively.

7. A SAR image positioning system based on laser point cloud and optical image, characterized in that: include: An automatic matching module, which is used to automatically match the connection points of the collected optical image and the SAR image to obtain image points with the same name; A block adjustment module is provided, wherein the block adjustment module is configured to use the same-name image points in the optical image and the elevation data of the same-name image points in the collected laser point cloud to perform a joint block adjustment to obtain a first affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the laser point cloud, including: performing a block adjustment on the optical image using a rational function algorithm to obtain a first initial affine transformation coefficient; performing a joint block adjustment on the optical image and the elevation data in the laser point cloud to iterate the first initial affine transformation coefficient to obtain a first affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the laser point cloud; A free network adjustment module is provided, wherein the free network adjustment module is used to perform free network adjustment on the optical image by using the first affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the laser point cloud to obtain second affine transformation coefficients and the three-dimensional coordinates of the same-name image points on the optical image, including: establishing a first mapping relationship between the image space coordinates of the optical image and the corresponding object space coordinates by a rational function algorithm; when a system error compensation polynomial is taken once, establishing a first error equation of regional block adjustment based on RFM by using an image space affine transformation model based on image space compensation according to the first mapping relationship; and solving the first error equation to obtain the second affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the optical image; A control block adjustment module is provided, wherein the control block adjustment module is used to perform a control block adjustment on the SAR image using the second affine transformation coefficients and the three-dimensional coordinates of the same-name image points on the optical image to obtain third radiometric transformation coefficients and the three-dimensional coordinates of the same-name image points on the SAR image. The control block adjustment module comprises: using the three-dimensional coordinates of the same-name image points on the optical image as control points, and establishing a second mapping relationship between image space coordinates and corresponding object space coordinates in the SAR image through a rational function algorithm; when a system error compensation polynomial is taken once, establishing a second error equation for block adjustment based on RFM through an image space affine transformation model based on image space compensation according to the second mapping relationship; and using the coordinate values ​​of the control points as true values ​​to solve the second error equation to obtain the third radiometric transformation coefficients and the three-dimensional coordinates of the same-name image points on the SAR image.

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