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

Abstract

The invention provides an SAR (Synthetic Aperture Radar) image positioning method and system based on laser point cloud and an optical image. The method comprises the following steps: automatically matching connection points of the acquired optical image and SAR image to obtain same-name image points; carrying out joint block adjustment by adopting a homonymous image point in the optical image and elevation data of the homonymous image point in the laser point cloud to obtain a first affine transformation coefficient and a three-dimensional coordinate of the homonymous image point on the laser point cloud; performing free net adjustment on the optical image through the three-dimensional coordinates of the homonymous image points on the laser point cloud to obtain a second affine transformation coefficient and the three-dimensional coordinates of the homonymous image points on the optical image; and according to the three-dimensional coordinates of the homonymous image points on the optical image, performing control block adjustment on the SAR image to obtain the three-dimensional coordinates of the homonymous image points on the SAR image and an accurate SAR image affine transformation coefficient. According to the method, multi-source data are considered, and the overall positioning precision of the SAR image is improved in a joint positioning mode.

Description

Technical Field

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

[0002] At present, my country's remote sensing surveying and mapping satellites have achieved global observation and provided a large number of high-quality surveying and mapping products for the construction of the "Belt and Road". However, due to the difficulty in obtaining high-precision ground control data in some areas, it is difficult to further improve the accuracy of stereo measurement. The emergence of products such as laser altimetry data, optical stereo satellite images, and SAR satellite images has made it possible to conduct high-precision stereo measurement of terrain. In the fields of geographic information systems (GIS), civil engineering, and surveying and mapping, adjustment methods are usually used to solve various measurement and positioning error problems in order to improve the accuracy and reliability of measurement data and ensure high positioning accuracy.

[0003] Adjustment method is a mathematical method used to process measurement data and adjust measurement results. In practical applications, it can effectively process various complex measurement data, solve measurement errors, inconsistent observation accuracy, incomplete data and other problems, so as to improve the reliability and accuracy of measurement results, and provide important basic data support for engineering design, map making and spatial data analysis. Among them, joint adjustment is a commonly used adjustment method, which can comprehensively process multiple types of observation data and optimize and adjust multiple measurement parameters at the same time. 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, so as to obtain more accurate and consistent measurement results. The basic principle is to establish a mathematical model, take into account various observation data and the relationship between them, and then use the least squares method or other mathematical optimization techniques to adjust this comprehensive model so that each observation data can achieve optimal consistency under unified parameters.

[0004] When performing joint adjustment, the positioning accuracy is affected by the huge differences in initial positioning accuracy, imaging mechanism, processing methods, etc. between laser altimetry data, optical stereo satellite images, SAR satellite images, and other measurement data. For example, laser altimetry data can be used as a control benchmark to improve the overall positioning accuracy of multi-source data. However, since the satellite-borne laser is not equipped with a footprint camera, the specific location of the laser altimetry point cannot be determined, making it difficult to meet the consistency of the object-image coordinates. This makes the joint adjustment optimization strategy of laser altimetry data a difficult point in the joint positioning of multi-source data. Therefore, how to achieve the joint adjustment and positioning solution of multi-source heterogeneous data is the key to improving the positioning accuracy of multi-source data. 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. The method fully considers the use of multi-source data such as optical images, satellite-borne laser point cloud data, open source DEM, etc., combined with the high positioning accuracy of optical images themselves (such as Tianhui-3 satellite) and 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 matching 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 joint block adjustment to obtain first affine transformation coefficients and 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 second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image;

[0009] S4. Performing control area block adjustment on the SAR image by 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, extracting features and performing logical block division on the bottom layer of the image pyramid by using a Fonstner operator to obtain a plurality of logical blocks, and mapping the plurality of logical blocks to other layers in the image pyramid, respectively, wherein each of the logical blocks includes a plurality of feature points;

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

[0014] S14, using the initial radiation transformation parameters and the logic block, automatically matching the connection points using the correlation coefficient method and the least square 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, iterating the first initial affine transformation coefficient through 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 points with the same name on the laser point cloud.

[0018] Furthermore, in the above step S22, the first initial affine transformation coefficient is iterated through 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-projection image points;

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

[0021] S223, by matching the adjusted object-space coordinates of the laser height measurement point and 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 coordinates of the laser height measurement point and the corresponding image 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 height measurement point as control points;

[0024] S2232, respectively assigning a weight to the error equations of the object space initial coordinates and the image space coordinates of the laser height measurement point, taking the elevation value of the image space coordinates as the true value, and correcting 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 height measurement 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 is performed 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, including:

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

[0027] S32, when the system error compensation polynomial is taken once, according to the first mapping relationship, the first error equation of the regional block adjustment based on RFM is established by the image space affine transformation model based on image space compensation;

[0028] S33, solving 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: Among them, (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 same-name image points 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, the control area block adjustment is performed on the SAR image by using the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image to obtain the third radiometric transformation coefficient and the three-dimensional coordinates of the image points with the same name on the SAR image, including:

[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-side coordinates and the corresponding object-side coordinates in the SAR image through a rational function algorithm;

[0032] S42, when the system error compensation polynomial is taken once, according to the second mapping relationship, the second error equation of the regional block adjustment based on RFM is established by the image space affine transformation model based on image space compensation;

[0033] S43. Taking the coordinate value of the control point as the true value, solving the second error equation, and obtaining a third radiation transformation coefficient and the three-dimensional coordinates of the image point 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: Among them, (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, 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, including an automatic matching module, a regional network adjustment module, a free network adjustment module and a control regional network 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 regional 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 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;

[0038] The free network adjustment module is used to perform free network adjustment on the optical image through the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud, so as 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 through the second affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the optical image, so as to obtain the third radiometric transformation coefficient 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 processor implements any of the above-mentioned SAR image positioning methods based on laser point cloud and optical image, so as to achieve the purpose of effectively improving the positioning accuracy of the SAR image.

[0041] An embodiment of the present invention further 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 cloud and optical image, 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 SAR image positioning method of the present invention, firstly, the optical image and the SAR image are automatically matched to obtain multiple image points of the same name; secondly, the coordinates of the image points of the same name in the optical image and their elevation data in the laser point cloud are combined with regional network adjustment to obtain the first affine transformation coefficient and the three-dimensional coordinates of each image point of the same name on the laser point cloud; thirdly, the optical image is adjusted by free network adjustment through 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 the second affine transformation coefficient and the three-dimensional coordinates of the image points of the same name on the optical image; finally, the SAR image is adjusted by the second affine transformation coefficient and the three-dimensional coordinates of the image points of the same name on the optical image to obtain the third radiation transformation coefficient and the three-dimensional coordinates of each image point of 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 height measurement data of the point cloud data as the control reference data through joint positioning. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0044] Figure 1 A flowchart of a 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 image 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 image 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, automatic matching module; 302, regional network adjustment module; 303, free network adjustment module; 304, control regional network adjustment module; 401, memory; 402, 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 implementation methods of the present application through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the content 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 implementation methods, 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 the following embodiments and the features of the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0051] Laser point cloud, also known as laser altimetry data, is a large amount of discrete point data obtained by the laser radar system, which is used to describe the three-dimensional spatial information of the surface of the object. By emitting laser pulses through the laser radar and recording the time and intensity of the laser pulse return, the position and shape of the surface of the target object can be accurately measured, thereby generating high-density three-dimensional point cloud data. Among them, laser altimetry (LiDAR, Light Detection and Ranging) is a method of obtaining surface elevation information through laser technology. By emitting laser pulses and recording their reflection time, the elevation measurement and three-dimensional modeling of the surface can be achieved.

[0052] Optical images, also known as optical stereo satellite images, are image data of the earth's surface acquired using optical cameras or optical remote sensors, usually including visible light images and near-infrared band images. These image data record the surface features and object information of the earth in digital form, and can be used in map making, land use classification, environmental monitoring, urban planning and other fields. SAR images, 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 images have many unique characteristics, which make them widely used in earth observation and environmental monitoring.

[0053] Object-image coordinates usually refer to the two coordinate systems of object coordinates and image coordinates in optical imaging. Object coordinates refer to the coordinate position of points on the object on the actual object. In the field of geographic information system (GIS) or remote sensing, object coordinates can be actual geographic coordinates on the earth's surface, such as longitude and latitude. Image coordinates refer to the coordinate position of the image formed by the object in the imaging process on the imaging plane. In remote sensing image processing, image coordinates are usually row and column coordinates of pixels, which are used to indicate the position on the digital image.

[0054] The embodiment of the present invention discloses a SAR image positioning method based on laser point cloud and optical image. The method uses laser altimetry data, optical stereo satellite image and SAR satellite image data for joint adjustment, fully considers the use of existing optical stereo satellite image, satellite-borne laser altimetry data, open source DEM and other multi-source data, combines the high positioning accuracy of optical stereo image itself (such as Tianhui-3 satellite), satellite-borne laser radar data can be used as control points, etc., to improve the positioning accuracy of SAR image.

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

[0056] S1, automatically matching 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 joint block adjustment to obtain first affine transformation coefficients and 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 second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image;

[0059] S4. Performing control area block adjustment on the SAR image by 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, extracting features from the bottom layer of the image pyramid using a Fonstner operator so that the distribution of feature points is as uniform as possible, and then performing logical block division to obtain a plurality of logical blocks, so that each logical block has a number of feature points and the number remains roughly the same, and mapping the plurality of logical blocks to other layers in the image pyramid, respectively.

[0063] S13, calculating the initial radiometric transformation parameters from the optical image to the SAR image according to the corner point coordinates and geographic coordinate mapping at the bottom layer of the image pyramid, wherein the initial radiometric transformation parameters can be expressed as a0, a1, a2, b0, b1, b2.

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

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

[0066] S141, at the top layer of the image pyramid, according to the obtained 6 initial affine transformation parameters (a0, a1, a2, b0, b1, b2), the matching window corresponding to the reference window (corresponding to a logical block) of the SAR image and the optical image is calculated, and the matching window is image resampled 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 search window after resampling by using the correlation coefficient method for image matching, 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 that were successfully matched in step S142 are retained together.

[0069] S144, the matching result of step S143 is transferred to the next layer of the image pyramid, which only needs to be transferred on the search image, and then return to steps S141 to S143 to perform the same matching with the top layer of the image pyramid. At this time, it should be noted that the affine transformation parameters updated in step S143 need to be used for image resampling each time. Then repeat step S144 until the matching result is transferred to the bottom layer 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 squares matching to improve the accuracy of image matching. Finally, use the affine transformation formula Calculate the corresponding image points of the same name on the search image, where (x, y) and (X, Y) are the coordinates of the corresponding image points of 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 regional block adjustment to obtain the first affine transformation coefficient 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 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.

[0075] When the above step S21 is implemented, the rational function algorithm used can also be called the rational function model (RFM), which is a universal sensor model applicable to various sensors and is a commonly used mathematical model for high-resolution optical satellite sensors. RFM takes the object-side three-dimensional coordinates of the optical image as the independent variable and the image-side coordinates of the optical image as the dependent variable, and 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 coordinates:

[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] 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 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 combined with a block adjustment, the first initial affine transformation coefficient is iterated, and 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, 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-projection image points;

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

[0086] S223, by matching the adjusted object-space coordinates of the laser height measurement point and 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 coordinates of the laser height measurement point with the corresponding image 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 height measurement point as control points;

[0089] S2232, respectively assigning a weight to the error equations of the object space initial coordinates and the image space coordinates of the laser height measurement point, taking the elevation value of the image space coordinates as the true value, and correcting 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 height measurement 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 1, and the weight of the error equation of the image space coordinates of the laser height measurement point can 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 The correction value is adjusted until the plane coordinate correction value of the laser height measurement point P is less than the limit error, and 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 inconsistency problem 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, establishing a first mapping relationship between the image-side coordinates of the optical image and the corresponding object-side coordinates through a rational function algorithm, wherein the first mapping relationship can be expressed as: Among them, 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 shall not exceed 3.

[0094] S32. When the system error compensation polynomial is taken once, according to the first mapping relationship, a first error equation of the regional block adjustment based on RFM 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: Among them, (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 same-name image points 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, V represents the residual vector of the row and column coordinate observation values ​​of the image point; X is the correction vector of the image coordinate system error compensation parameters (i.e., 6 affine transformation parameters); Y is the correction vector of the ground 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 by the initial value calculation; P is the weight matrix. Among them, X = [Δa0Δa1Δa2Δb0Δb1Δb2] T , Y=[ΔLat,ΔLon,ΔHeight] T , V=[v x v y ] T .

[0097] S33, solving 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 specifically, 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 both the affine transformation parameters in the optical image and their corresponding object-space geodetic coordinates, 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 by using the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image to obtain the third radiometric transformation coefficient 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, and establishing a second mapping relationship between the image-side coordinates and the corresponding object-side coordinates in the SAR image by a rational function algorithm, wherein the second mapping relationship is the same as the first mapping relationship, and can be expressed as: Among them, 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 shall not exceed 3.

[0101] S42. When the system error compensation polynomial is taken once, according to the second mapping relationship, a second error equation of the regional block adjustment based on RFM is established through 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: Among them, (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 same-name image points 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 error equation, and can be expressed as: V = AX + BY-L, P, where V represents the residual vector of the row and column coordinate observation values ​​of the image point; X is the correction vector of the image coordinate system error compensation parameters (i.e., 6 affine transformation parameters); Y is the correction vector of the ground 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 by the initial value calculation; and P is the weight matrix. Among them, X = [Δa0 Δa1 Δa2 Δb0 Δb1Δb2] T , Y=[ΔLat,ΔLon,ΔHeight] T , V=[v x vy ] T .

[0104] S43. Taking the coordinate value of the control point as the true value, solving the second error equation, and obtaining a third radiation transformation coefficient and the three-dimensional coordinates of the image point 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 numbers 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 network adjustment model of step S4 adopts the RFM model identical with the non-controlled area network adjustment in step S3, but for the controlled area network adjustment model, the coordinate values ​​of the control points in its error equation are true values, and when performing transformation parameter solution, there is no need to use the control point coordinates as unknowns to perform parameter solution.

[0107] Based on the same inventive concept, a joint adjustment system of laser point cloud, optical image and SAR image is also provided in the embodiment of the present invention, as described in the following embodiment. 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 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 embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0108] Figure 3 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 network adjustment module 302, a free network adjustment module 303 and a control regional network 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 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 a first affine transformation coefficient and the 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 through the first affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the laser point cloud to obtain the second affine transformation coefficient and the three-dimensional coordinates of the same-name image points on the optical image;

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

[0113] The embodiment of the present invention achieves the following technical effects: the SAR image positioning method of the present invention firstly automatically matches the connection points of the optical image and the SAR image to obtain multiple image points of the same name; secondly, the coordinates of the image points of the same name in the optical image and their elevation data in the laser point cloud are combined with the regional network adjustment to obtain the first affine transformation coefficient and the three-dimensional coordinates of each image point of the same name on the laser point cloud; thirdly, the optical image is adjusted by 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 the second affine transformation coefficient and the three-dimensional coordinates of the image points of the same name on the optical image; finally, the SAR image is adjusted by the second affine transformation coefficient and the three-dimensional coordinates of the image points of the same name on the optical image to obtain the third radiation transformation coefficient and the three-dimensional coordinates of each image point of 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 height measurement data of the point cloud data as the control reference data through the 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 cloud and optical image.

[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. 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 disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

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

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope 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 images and SAR images to obtain image points with the same name; 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, a joint block adjustment is performed to obtain first affine transformation coefficients and three-dimensional coordinates of the image points with the same name on the laser point cloud; 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 second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image; The SAR image is adjusted by controlling the area block using the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image, so as to obtain the third radiometric transformation coefficient and the three-dimensional coordinates of the image points with 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; Perform feature extraction and logical block division on the bottom layer of the image pyramid by using a Fonstner operator to obtain a plurality of logical blocks, and respectively map the plurality of logical blocks to other layers in 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 bottom corner coordinates and geographic coordinate mapping in the image pyramid; 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 method uses 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 to perform a joint block adjustment to obtain first affine transformation coefficients and three-dimensional coordinates of the image points with the same name on the laser point cloud, including: Performing block adjustment on the optical image by a rational function algorithm to obtain a first initial affine transformation coefficient; By performing joint block adjustment on the optical image and the elevation data in the laser point cloud, the first initial affine transformation coefficient is iterated to obtain the first affine transformation coefficient and the three-dimensional coordinates of the image points 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 method of performing 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 points of the image points with the same name in the optical image and the laser point cloud are matched 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 located at a forward-looking position or a downward-looking position as a reference point, and matching and adjusting the back-projected image points corresponding to the image points with the same name respectively; 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.

5. The SAR image positioning method based on laser point cloud and optical image according to claim 4, characterized in that: 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 coordinates of the laser height measurement point with the corresponding image coordinates, including: 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 respectively assigned to the error equations of the object space initial coordinates and the image space coordinates of the laser altimeter point, 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 value, 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.

6. The SAR image positioning method based on laser point cloud and optical image according to claim 1, characterized in that: The method of 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 the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image comprises: A first mapping relationship between the image-side coordinates of the optical image and the corresponding object-side coordinates is established by a rational function algorithm; When the system error compensation polynomial is taken once, according to the first mapping relationship, a first error equation of the regional block adjustment based on RFM is established through an image space affine transformation model based on image space compensation; The first error equation is solved to obtain second affine transformation coefficients and the three-dimensional coordinates of the image points with the same name on the optical image.

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

8. The SAR image positioning method based on laser point cloud and optical image according to claim 1, characterized in that: The method of performing control area block adjustment on the SAR image by using the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image to obtain the third radiometric transformation coefficient and the three-dimensional coordinates of the image points with the same name on the SAR image comprises: 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-side coordinates and the corresponding object-side coordinates in the SAR image is established through a rational function algorithm; When the system error compensation polynomial is taken once, according to the second mapping relationship, a second error equation of the regional block adjustment based on RFM is established through an image space affine transformation model based on image space compensation; The coordinate values ​​of the control points are taken as true values, and the second error equation is solved to obtain third radiation transformation coefficients and the three-dimensional coordinates of the image points with the same name on the SAR image.

9. The SAR image positioning method based on laser point cloud and optical image according to claim 8, characterized in that: The expression of the image-space affine transformation model based on image-space compensation is: Among them, (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, Δs and Δl are the correction vectors of s and l, respectively.

10. 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, wherein 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 a first affine transformation coefficient and a three-dimensional coordinate of the same-name image points on the laser point cloud; A free network adjustment module, wherein the free network adjustment module is used to perform free network adjustment on the optical image through the first affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the laser point cloud, so as to obtain a second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image; and a controlled area network adjustment module, wherein the controlled area network adjustment module is used to perform controlled area network adjustment on the SAR image through the second affine transformation coefficient and the three-dimensional coordinates of the image points with the same name on the optical image, so as to obtain a third radiation transformation coefficient and the three-dimensional coordinates of the image points with the same name on the SAR image.

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