Generalized control constraint based joint adjustment method for multi-source orbiter images of Mars

Abstract

The application relates to a kind of generalized control constraint-based joint adjustment method of Mars multi-source orbiters image, comprising: for low-resolution reference DOM and Mars orbiters optical image, extracting plane control point, and matching to obtain connecting point;Based on the fitting residual of Mars orbiters image rational function model, establish the image side compensation model of superimposed polynomial and cubic spline function and obtain initial compensation parameter;According to plane control point, connecting point and initial compensation parameter, combined with plane and elevation control constraint, the adjustment model is constructed, and the adjustment model is solved using adaptive weighting strategy, and the geometric positioning parameter of image is obtained.Compared with the prior art, the application can effectively improve the absolute and relative positioning accuracy of Mars multi-source orbiters image by carrying out multi-source image joint beam adjustment in cooperation with plane weak control constraint and elevation control constraint, and realizes the accurate registration of multi-source orbiters image.

Description

Technical Field

[0001] The present invention relates to the technical field of image registration, and in particular to a Mars multi-source orbiter image joint adjustment method based on generalized control constraints. Background Art

[0002] High-resolution Mars topography is a crucial tool for understanding the Martian topography and structure, conducting Mars exploration projects, and conducting scientific research. With the advancement of Mars mapping technology and the ongoing exploration activities, a large number of multi-mission, multi-coverage Mars remote sensing images have been accumulated.

[0003] Currently, among the orbiter optical images used to generate Mars planetary mapping products, the medium-resolution images that can cover the entire moon can only support the acquisition of meter-scale surface features at best, while the sub-meter-level high-resolution images used to generate high-precision local mapping products have limited coverage and uneven distribution. Therefore, the coordinated use of multi-source images is conducive to capturing more comprehensive and accurate information on the morphological features of Mars, and repeated observations of multi-source images can eliminate random errors, which is conducive to further improving the geometric positioning accuracy.

[0004] However, due to the limited accuracy of orbiter attitude and orbit determination, geometric positioning inconsistencies are common between images. Bundle adjustment is the primary method for eliminating these inconsistencies and enabling the coordinated use of multi-source imagery. In the absence of measured ground control points on Mars, it is necessary to introduce control information into the adjustment process to eliminate orbit and attitude errors, improve solution stability, and enhance the geometric positioning accuracy of the images. Using only the Mars Orbiter Laser Altimeter (MOLA) and its Digital Elevation Model (DEM) as a Martian reference for control adjustment is hampered by their limited resolution and planar accuracy. Planar inconsistencies can be minimized by selecting recognizable surface features in optical imagery and registering them with a priori DEMs. However, this method suffers from a low degree of automation and is affected by factors such as image distortion, jitter, imaging model differences, and illumination variations, making it difficult to directly obtain reliable feature points between the heterogeneous data for accurate registration. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a Mars multi-source orbiter image joint adjustment method based on generalized control constraints, which can improve the geometric positioning accuracy and consistency of multi-source orbiter images and quickly and automatically align multi-source images to a unified reference benchmark.

[0006] The object of the present invention can be achieved by the following technical solution: A Mars multi-source orbiter image joint adjustment method based on generalized control constraints, comprising the following steps:

[0007] S1. Extract plane control points from low-resolution reference DOM and Mars orbiter optical images, and match them to obtain connection points.

[0008] S2. Based on the fitting residuals of the rational function model of the Mars orbiter image, an image-space compensation model of superimposed polynomials and cubic spline functions is established and the initial compensation parameters are obtained;

[0009] S3. Based on the plane control points, connection points and initial compensation parameters, combined with the plane and elevation control constraints, an adjustment model is constructed, and an adaptive weighted strategy is used to solve the adjustment model to obtain the geometric positioning parameters of the image.

[0010] Furthermore, the step S1 includes the following steps:

[0011] S11. Preprocessing the Mars orbiter optical image to obtain a preprocessed optical image;

[0012] S12, extracting connection points between images by least squares template matching for the pre-processed optical image;

[0013] S13, combining the pre-processed optical image and the low-resolution reference DOM, outputting the crater center through image segmentation, mask segmentation, and crater fitting;

[0014] The crater centers are then screened, and the matched crater centers are obtained through point set registration to obtain the plane control points.

[0015] Furthermore, in step S12, for optical images with different illumination, least squares template matching is performed using Histogram of Oriented Phase Consistency (HOPC) as a similarity measure;

[0016] For optical images with consistent lighting conditions, normalized cross correlation (NCC) is used as the similarity measure for least squares template matching.

[0017] Furthermore, the specific process of step S13 includes:

[0018] Contrast-limited adaptive histogram equalization and gamma correction are used to perform radiometric enhancement on the pre-processed optical image and the low-resolution reference DOM to strengthen the crater rim.

[0019] The enhanced image is extracted and segmented using the SAM model (Segment Anything Model).

[0020] During crater extraction, segmentation masks that do not meet shape requirements are screened out. The area and perimeter of the mask are calculated to evaluate its roundness and ellipticity. When the ratio of perimeter to circumference and the ratio of major axis to minor axis both exceed the threshold of 0.9, the mask is judged to be circular; when the ratio of its major axis to actual area exceeds the threshold of 0.8, the mask is judged to be elliptical. The fitting accuracy of the circle or ellipse is evaluated through edge detection, and only masks with high fitting accuracy and accurate center position are retained.

[0021] An improved point set registration method that preserves global and local structures is then used to achieve the registration of crater positions between images. First, based on the prior geographic information of the craters, the target centers that lack candidate targets within the distance range are screened out. At the same time, local features are enhanced by constructing a feature descriptor that integrates the aspect ratio, fitting area ratio, and shape context features to ensure that the global and local structures are preserved during the matching process to complete the registration.

[0022] Furthermore, the specific process of step S2 is as follows:

[0023] The elevation of the image area is divided into several elevation surfaces. A three-dimensional virtual control grid of the image is established in the image space at set intervals as the image space virtual control points. Based on this, the image coordinates of the virtual control grid are projected onto each elevation surface according to a strict imaging model to obtain the object space virtual control points. The Rational Polynomial Coefficient (RPC) is then calculated by fitting and rational function model parameters. The rational function model is used to express the geometric relationship between the image space and the object space.

[0024] The image space compensation model of linear and nonlinear errors of images is constructed by using superimposed polynomials and cubic spline functions to obtain initial compensation parameters for compensating nonlinear image errors.

[0025] Furthermore, the expression of the rational function model is:

[0026]

[0027] Among them, (X o ,Y o ) and (X s ,Y s ) represent the offset and scale value of the normalized pixel coordinates (X, Y), (P, L, H) are the latitude, longitude and elevation of the normalized object point, Num s , Num l , Den s and Den l is a third-order polynomial with 20 coefficients, namely the rational function model parameter RPC.

[0028] Furthermore, the expression of the image space compensation model is:

[0029]

[0030] Among them, the parameters (e0, e1, e2, f0, f1, f2) represent the affine transformation used to compensate for the linear deviation. In view of the time variation characteristics caused by posture jitter, the average residual of the image points in the same row is used as the optimal estimate of the RPC nonlinear deviation. The corrected pixel coordinates (X ′ ,Y ′ ) is calculated as follows:

[0031]

[0032] in, are the undetermined parameters of the cubic spline function, which are used to compensate for nonlinear image errors. i=1,2…m-1 is the number of connection points.

[0033] Further, the process of constructing the adjustment model in step S3 includes:

[0034] The error equation is established and the regional block adjustment is performed. Based on the tie points, virtual control points, planar weak control constraints based on the crater center, terrain elevation constraints, and rational function model parameters RPC obtained by multi-source image matching, the error equation of the joint adjustment model is constructed, which includes the following four observation equations:

[0035]

[0036] Among them, TPs are connection points, ECPs are elevation control points, VCPs are virtual control points, PCPs are plane control points, V TPs is the observation equation of the connection point, V ECPs is the observation equation of the elevation control constraint, V PCPs is the observation equation of the plane control constraint, V VCPs is the observation equation of the virtual control point, t is the correction number of the adjustment parameter, x is the unknown ground coordinate of the connection point, matrices A and B are the error equation coefficient matrices, L is the constant term, P i,i＝1,2,3,4 For the corresponding weight, for the error equation, construct the normal equation, and use the least squares method to fit the object coordinates of the most approximate connection points;

[0037] Then, terrain constraints based on the prior DEM are introduced, and elevation control is achieved by minimizing the sum of squares of the distances from the connection points to the DEM surface, which corresponds to the second observation equation V ECPs The elevation error equation V h The expression is as follows:

[0038]

[0039] Where h0 is the elevation of the connection point, (lat0, lon0) is the geographic plane coordinate, and DEM (lat0, lon0) is the elevation value obtained by interpolation at (lat0, lon0). and are the slopes of the DEM in the latitude and longitude directions, respectively, and (Δlat, Δlon, Δh) are the corrections to the ground coordinates of the connection points;

[0040] A plane weak control constraint is constructed to incorporate terrain change information near the plane control point into the plane control constraint. The object space 3D coordinates of the control point are adaptively adjusted to achieve automatic registration between the image and the crater center in the DOM. The difference between the pixel coordinates of the plane control point and the corresponding 3D coordinates in the reference map is minimized by the following formula:

[0041]

[0042] Where (X, Y) is the pixel coordinate of the crater center in the orbiter image, RPC X (lat, lon, DEM) and RPC Y (lat, lon, DEM) is the pixel coordinate of the center of the crater obtained by reprojecting the object space coordinates in the reference DOM and DEM onto the image, and the third observation equation V PCPs The corresponding plane error equation v X and v0 is expressed as follows:

[0043]

[0044] in, as well as and To obtain the derivatives of the image coordinates with respect to latitude, longitude, and elevation for reprojection, (X0, Y0) is the pixel coordinate of the plane control point, and (ΔX′, ΔY′) is the difference between the image coordinates calculated by reprojecting the object space coordinates of the plane control point and the corresponding actual image coordinates;

[0045] Virtual control points (VCPs) are introduced to effectively reduce the degrees of freedom of the adjustment model. VCPs are strategically selected by predefined thresholds and grid sizes and introduced as weighted observations into the fourth observation equation V VCPs middle.

[0046] Furthermore, the process of adopting the adaptive weighting strategy to solve the adjustment model in step S3 includes:

[0047] According to the prior matching accuracy σ RPs , define the weight P1 of the connection point as:

[0048]

[0049] Taking the prior terrain as the control information, the error and point inconsistency caused by the terrain as the "gross error" in the control information, and in order to quantitatively reflect the impact of the terrain on the accuracy, the weight function of the elevation control constraint is defined as follows:

[0050]

[0051] Among them, img res is the image resolution, d h is the elevation difference, h mean is the average elevation difference of the data set, σ h is the standard deviation of the elevation difference. When the elevation difference is less than σ h When the weight is determined by the image resolution and the standard deviation of the elevation difference, the height difference is greater than three times σ h When , the weight gradually approaches zero;

[0052] Calculate the scale factor for each adjustment area separately and where Num tie is the number of connection points, Num pcp is the number of plane control points, Num vcp is the number of virtual control points, considering the crater radius r and the fitting standard deviation f, the weight function of the plane control constraint is defined as follows:

[0053]

[0054]

[0055] Among them, d p is the plane difference, p mean is the average value of the plane difference, σ p is the standard deviation of the plane difference, R max is the normalized radius factor, F max is the normalized fitting factor, the smoothing function σ f As f approaches the set threshold f threshold Gradually reduce the weight to zero, the steepness of the transition curve is controlled by the constant k, when the standard deviation is lower than f threshold When the value is higher than f, the weight decreases rapidly. On the contrary, when the value is higher than f threshold When , the weight decreases more slowly;

[0056] A dynamic weighting function is established to alleviate the imbalance between the number of virtual control points. The unknown ground coordinates of the connection points are calculated based on the least squares method to solve the refined geometric positioning parameters of each image.

[0057] Furthermore, the expression of the dynamic weighting function is:

[0058]

[0059] Where N represents the number of iterations.

[0060] Compared with the prior art, the present invention has the following advantages:

[0061] The present invention first extracts plane control points and matches the connection points from the low-resolution reference DOM and the Mars orbiter optical image; then, based on the fitting residuals of the Mars orbiter image rational function model, an image square compensation model of superimposed polynomials and cubic spline functions is established and initial compensation parameters are obtained; then, based on the plane control points, connection points and initial compensation parameters, an adjustment model is constructed in combination with plane and elevation control constraints, and an adaptive weighted strategy is used to solve the adjustment model to obtain the geometric positioning parameters of the image. Thus, by coordinating the plane weak control constraints and elevation control constraints to carry out the multi-source image joint bundle method adjustment, the absolute and relative positioning accuracy of the Mars multi-source orbiter image can be effectively improved, and the precise registration of the multi-source orbiter image can be achieved.

[0062] When extracting plane control points and matching connection points on the Martian surface, the present invention uses contrast-limited adaptive histogram equalization and gamma correction to perform radiation enhancement on subsatellite images and low-resolution reference DOM to strengthen the crater edges, thereby facilitating the subsequent extraction and segmentation of the image using the SAM model. On the other hand, the area and perimeter of the mask are calculated to evaluate its roundness and ellipticity. The fitting accuracy of the circle or ellipse is then evaluated through edge detection, and only masks with high fitting accuracy and accurate center position are retained. In addition, to address the problem that direct feature point matching is difficult due to imaging differences between heterogeneous images, an improved point set registration method that preserves global and local structures is adopted to achieve accurate registration of crater positions between images.

[0063] The present invention adopts a rational function model to express the geometric relationship between the image space and the object space, which is conducive to the joint processing of multi-source images. In addition, to address the problem that traditional RPC models and low-order polynomials cannot effectively compensate for nonlinear errors, superimposed polynomials and cubic spline functions are used to construct a unified compensation model for image linear and nonlinear errors. This effectively considers the linear and nonlinear distortions of orbiter images and can ensure the robustness of the model during adjustment calculations.

[0064] The present invention constructs an adjustment model that combines weak planar control constraints, elevation control constraints, an image square compensation model, image tie points, and virtual control points. The model establishes an elevation error equation by minimizing the elevation difference between the tie point triangulation result and the prior DEM. A planar error equation is established by minimizing the planar distance using topographic variation information at the crater center. Reference data is used to construct an adaptive weighting function to improve adjustment accuracy and convergence speed, thereby correcting image sensor and track errors.

[0065] Taking into account the different intersection conditions of the connection points between multi-source images, the present invention may cause non-convergence or instability during the adjustment process. Therefore, an adaptive weighting strategy combining connection points, elevation constraints, planar weak control constraints and virtual control points is designed to solve the adjustment model. This strategy can ensure the robustness of the adjustment and dynamically coordinate various constraints in combination with the introduced adaptive weighting function, effectively improving the relative and absolute geometric positioning accuracy of multi-source images. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 Schematic diagram of the method flow of the present invention;

[0067] Figure 2 Schematic diagram of the application process of the embodiment;

[0068] Figure 3 Schematic diagram of crater extraction, screening and registration in the embodiment;

[0069] Figure 4a Schematic diagram of elevation control constraints in the embodiment;

[0070] Figure 4b Schematic diagram of plane weak control constraint in the embodiment;

[0071] Figure 5a The comparison of geometric consistency between the DOM before and after the adjustment of Gale crater in the embodiment;

[0072] Figure 5b The geometric consistency comparison between the DOM before and after the adjustment of the Jezero crater in the embodiment is shown;

[0073] Figure 5c The geometric consistency comparison between DOM before and after adjustment of the Utopia Plain region in the embodiment;

[0074] Figure 5d This is a 6-meter / pixel CTX image mosaic of the Gale crater area in the embodiment;

[0075] Figure 5e This is a 6-meter / pixel CTX image mosaic of the Jezero crater area in the embodiment;

[0076] Figure 5f This is a mosaic of 6m / pixel CTX images of the Utopia Plain area in the embodiment;

[0077] Figure 5g This is a HiRISE mosaic of Gale crater at 1 meter / pixel in the embodiment;

[0078] Figure 5h This is a HiRISE mosaic of the Jezero crater area at 1 meter / pixel in the embodiment;

[0079] Figure 5i This is a HiRISE mosaic image of the Utopia Planitia region at 1 meter / pixel in the embodiment. DETAILED DESCRIPTION

[0080] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0081] Example

[0082] like Figure 1 As shown in FIG, a joint adjustment method for Mars multi-source orbiter images based on generalized control constraints includes the following steps:

[0083] S1. Extract plane control points from low-resolution reference DOM and Mars orbiter optical images, and match them to obtain connection points.

[0084] S2. Based on the fitting residuals of the rational function model of the Mars orbiter image, an image-space compensation model of superimposed polynomials and cubic spline functions is established and the initial compensation parameters are obtained;

[0085] S3. Based on the plane control points, connection points and initial compensation parameters, combined with the plane and elevation control constraints, an adjustment model is constructed, and an adaptive weighted strategy is used to solve the adjustment model to obtain the geometric positioning parameters of the image.

[0086] This embodiment applies the above solution, such as Figure 2 As shown in the figure, it mainly includes three aspects: plane control point extraction and connection point matching, image space compensation model of superposition polynomial and cubic spline function, and adjustment method of comprehensive geometric constraints and adaptive weighting strategy, which can improve the geometric positioning accuracy and consistency of multi-source orbiter images and quickly and automatically align multi-source images to a unified reference benchmark.

[0087] Specifically:

[0088] 1. Plane control point extraction and connection point matching

[0089] Given the significant differences in imaging patterns between the low-resolution reference DOM and Mars orbiter optical imagery, making effective matching difficult, this proposal proposes an efficient strategy for extracting and matching control points from the Martian surface plane to obtain reliable inter-image relationships and control points. Using contrast-limited adaptive histogram equalization and gamma correction, radiometric enhancement is performed on the sub-satellite imagery (i.e., vertically captured imagery included in the preprocessed optical imagery) and the low-resolution reference DOM to enhance crater rims, facilitating subsequent image extraction and segmentation using the SAM (Segment Anything Model).

[0090] During crater extraction, considering that most craters are circular or elliptical, segmentation masks that do not conform to these shapes are screened out. The specific strategy employed is to evaluate the circularity and ellipticity of the mask by calculating its area and perimeter. A mask is considered circular when both the perimeter-to-circumference ratio and the major-minor axis ratio exceed a threshold of 0.9. A mask is considered elliptical when the ratio of its major-minor axis area to the actual area exceeds a threshold of 0.8. Edge detection is used to assess the accuracy of the circle or ellipse fit, retaining only masks with high fit accuracy and accurately positioned centers.

[0091] To address the difficulty of directly matching feature points between heterogeneous images due to imaging discrepancies, this solution employs an improved point set registration method that preserves both global and local structure to achieve precise registration of crater locations between images. First, based on prior crater geographic information, target centers with insufficient candidate targets within the distance range are eliminated. Furthermore, by constructing a feature descriptor that integrates the aspect ratio, fitted area ratio, and shape context, local features are enhanced to ensure that global and local structure are preserved during the matching process. Figure 3 The results of crater extraction, screening, and registration are shown. Least squares template matching is performed using the Histogram of Oriented Phase Congruency (HOPC) as a similarity measure between CTX and HiRISE images with different illumination conditions. When illumination conditions are relatively consistent, least squares template matching is performed using the Normalized Cross Correlation (NCC) as a similarity measure to reliably extract connection points between the images.

[0092] 2. Image-Square Compensation Model of Superimposed Polynomials and Cubic Spline Functions

[0093] This scheme uses a rational function model to express the geometric relationship between the image space and the object space, which is conducive to the joint processing of multi-source images. Its expression is:

[0094]

[0095] Among them, (X o ,Y o ) and (X s ,Y s ) represent the offset and scale value of the normalized pixel coordinates (X, Y), (P, L, H) are the latitude, longitude and elevation of the normalized object point, Num s , Num l , Den s and Den lThis is a third-order polynomial with 20 coefficients, also known as the rational polynomial coefficient (RPC). The RPC is calculated by dividing the image area's elevation into several elevation surfaces and establishing a three-dimensional virtual control grid at regular intervals in the image space as the image-space virtual control points. Based on this, the virtual control grid image coordinates are projected onto each elevation surface according to a strict imaging model to obtain the object-space virtual control points. The RPC can then be calculated by fitting the grid.

[0096] To address the problem that traditional RPC models and low-order polynomials cannot effectively compensate for nonlinear errors, this solution uses superimposed polynomials and cubic spline functions to construct a unified compensation model for image linear and nonlinear errors, ensuring the robustness of the model during adjustment calculations. The specific form of the compensation model is as follows:

[0097]

[0098] Among them, the parameters (e0, e1, e2, f0, f1, f2) represent the affine transformation used to compensate for the linear deviation. In view of the time variation characteristics caused by posture jitter, the average residual of the image points in the same row is used as the optimal estimate of the RPC nonlinear deviation. The corrected pixel coordinates (X ′ ,Y ′ ) is calculated as follows:

[0099]

[0100] in, are the undetermined parameters of the cubic spline function, which are used to compensate for nonlinear image errors. i=1,2…m-1 is the number of connection points.

[0101] 3. Adjustment method based on integrated geometric constraints and adaptive weighting strategy

[0102] (1) Construction of adjustment model combining plane and elevation control constraints

[0103] The error equation is established and the regional block adjustment is performed. The error equation of the joint adjustment model is constructed based on the tie points, virtual control points, plane weak control constraints based on the crater center, terrain elevation constraints, and rational function model parameters obtained by multi-source image matching. It mainly consists of the following four observation equations:

[0104]

[0105] Among them, TPs are connection points, ECPs are elevation control points, VCPs are virtual control points, PCPs are plane control points, V TPs is the observation equation of the connection point, V ECPs is the observation equation of the elevation control constraint, V PCPsis the observation equation of the plane control constraint, V VCPs is the observation equation of the virtual control point. t is the adjustment parameter correction number, and x is the unknown ground coordinate of the connection point. Matrices A and B are the error equation coefficient matrices, L is the constant term, and P i,i＝1,2,3,4 For the error equation, construct the normal equation and use the least squares method to fit the object coordinates of the most approximate connection points.

[0106] In order to solve the problem of difficulty in obtaining measured control points on Mars and limited absolute positioning accuracy, this solution introduces terrain constraints based on the prior DEM and realizes elevation control by minimizing the sum of squares of the distances from the connection points to the DEM surface. Figure 4a As shown. Corresponding to the second observation equation V ECPs The elevation error equation V h The expression is as follows:

[0107]

[0108] Where h0 is the elevation of the connection point, (lat0, lon0) is the geographic plane coordinate, and DEM (lat0, lon0) is the elevation value obtained by interpolation at (lat0, lon0). and are the slopes of the DEM in the latitude and longitude directions, respectively, and (Δlat, Δlon, Δh) are the corrections to the ground coordinates of the connection points.

[0109] In order to solve the problem of inconsistent fitting positions due to imaging differences of multiple sources in the process of extracting the crater center, a plane weak control constraint is constructed. By introducing the terrain change information near the plane control point into the plane control constraint, the object space 3D coordinates of the control point are adaptively adjusted, thereby achieving automatic registration between the image and the crater center in the DOM, and improving the absolute plane positioning accuracy, such as Figure 4b The difference between the pixel coordinates of the plane control points and the corresponding three-dimensional coordinates in the reference map can be minimized by the following formula:

[0110]

[0111] Where (X, Y) is the pixel coordinate of the crater center in the orbiter image, RPC X (lat,lon,DEM) and RPC Y (lat, lon, DEM) is the pixel coordinate of the center of the crater obtained by reprojecting the object space coordinates in the reference DOM and DEM onto the image. Therefore, it is consistent with the third observation equation V PCPs The corresponding plane error equation v X and v Y The expression is as follows:

[0112]

[0113] in, as well as and Obtain the derivatives of the image coordinates with respect to latitude, longitude, and elevation for reprojection. (X0, Y0) are the pixel coordinates of the plane control point, and (ΔX′, ΔY′) are the differences between the image coordinates calculated by reprojecting the object space coordinates of the plane control point and the corresponding actual image coordinates.

[0114] In addition, virtual control points (VCPs) are introduced to effectively reduce the degrees of freedom of the adjustment model, thereby solving the rank deficiency problem of the normal equation coefficient matrix and improving the robustness and convergence speed of the adjustment. VCPs are strategically selected by predefined thresholds and grid sizes and introduced as weighted observations into the fourth observation equation V VCPs middle.

[0115] (2) Adaptive weighted strategy method

[0116] Due to the different intersection conditions of tie points between multi-source images, non-convergence or instability may occur during the adjustment process. To ensure the robustness of the adjustment, it is necessary to develop an adaptive weighting strategy that combines tie points, elevation constraints, plane weak control constraints, and virtual control points. The weight P1 of the tie point can be calculated based on the prior matching accuracy σ TPs The definition is as follows:

[0117]

[0118] Among them, σ TPs is the prior matching accuracy.

[0119] The prior terrain is used as control information, and its control effect varies with the terrain undulation. In flat or gently sloping areas, due to the small terrain undulation, the plane coordinate correction number is small, and the control effect on the plane displacement is weak; while in areas with obvious terrain undulation, the plane coordinate correction number increases and the control effect is enhanced. The impact of local terrain changes on the adjustment results is reflected by formulas (5) and (7). The errors and point inconsistencies caused by the terrain can be regarded as "gross errors" in the control information and need to be effectively eliminated during the adjustment process. In order to quantitatively reflect the impact of terrain on accuracy, the weight function of the elevation control constraint is defined as follows:

[0120]

[0121] Among them, img res is the image resolution, d h is the elevation difference, h mean is the average elevation difference of the data set, σ h is the standard deviation of the elevation difference. hWhen the weight is determined by the image resolution and the standard deviation of the elevation difference, the height difference is greater than three times σ h , the weight gradually approaches zero.

[0122] Due to the imbalance in the number of virtual control points, plane control points and tie points, it may happen that the elevation control is strong and the plane control is weak. Therefore, the scale factor is calculated for each adjustment area separately. and where Num tie is the number of connection points, Num pcp is the number of plane control points, Num vcp is the number of virtual control points. Considering the crater radius r and the fitting standard deviation f, the weight function of the plane control constraint is defined as follows:

[0123]

[0124] Among them, d p is the plane difference, p mean is the average value of the plane difference, σ p is the standard deviation of the plane difference. R max is the normalized radius factor, F max is the normalized fitting factor. Smoothing function σ f As f approaches the set threshold f threshold (Empirically set to 0.35) Gradually reduce the weight to zero. The steepness of the transition curve is controlled by the constant k, which is set to 9. When the standard deviation is lower than f threshold When the value is higher than f, the weight decreases rapidly. theeshold When , the weight decreases more gently, effectively alleviating the impact of outliers and thus enhancing the robustness of the adjustment process.

[0125] In order to speed up the convergence and avoid the problem of weakening the plane control effect of the crater center due to assigning fixed weights to virtual control points, a dynamic weighting function is established to alleviate the imbalance between the number of virtual control points. The specific expression is as follows:

[0126]

[0127] Where N is the number of iterations. The unknown ground coordinates of the tie points are calculated using the least squares method, thereby solving the refined geometric positioning parameters of each image.

[0128] To verify the effectiveness of this solution, this example selected HiRISE (High-Resolution Imaging Science Experiment) and CTX (Context Camera) images from three regions as experimental data. Region 1 is near Gale Crater, Region 2 is near Jezero Crater, and Region 3 is near Utopia Planitia. Eight images were selected from each region. The experiment used a THEMIS mosaic with a resolution of 100 meters per pixel and a HRSC / MOLA blended DEM with a resolution of 200 meters per pixel as planar and elevation reference data to obtain reliable control information.

[0129] The rational function model before and after generalized control constraint adjustment was used to project the triangulated object-space 3D coordinates of the matching tie points into image space, and the residuals between the back-projected image coordinates and the actual image coordinates in the row and column directions of the image were calculated. Table 1 summarizes the image-space compensation and the root mean square error of the image-space backprojection of the tie points before and after adjustment. The results show that because the initial RPCs are affected by linear and nonlinear deviations, the root mean square error of the reprojection residual is a maximum of approximately 13 pixels in the row direction and a maximum of approximately 7 pixels in the column direction. After applying image-space compensation, the geometric consistency of the image coordinates improved, but it still did not meet the requirements of high-precision applications. After generalized control constraint adjustment, the root mean square error was reduced to less than 0.5 pixels, improving the geometric consistency of the image and effectively compensating for the linear and nonlinear deviations in the RPC.

[0130] Table 1. Root mean square error of back-projection adjustment with generalized control constraints (unit: pixel)

[0131]

[0132] The relative positioning root mean square (RMS) errors (RMSs) before and after the generalized control constraint adjustment are calculated, as shown in Table 2. Relative positioning error is the difference between the object-space 3D coordinates corresponding to the triangulation of each pair of tie points between multiple image pairs. Before adjustment, the horizontal RMS error of the multi-source imagery was approximately 80 meters, exceeding 200 meters in some areas. The elevation RMS error ranged from 15 to 110 meters. With the generalized control constraint adjustment, both the horizontal and elevation RMS errors were reduced, with the horizontal RMS error below 7 meters and the elevation RMS error below 3 meters.

[0133] Table 2. Root mean square error of relative positioning before and after generalized control constraint adjustment (unit: meter)

[0134]

[0135] Twenty checkpoints were manually measured from the reference DEM and DOM as their true object space three-dimensional coordinates. The absolute positioning accuracy of the image before and after adjustment was calculated by calculating the difference between the object space three-dimensional coordinates obtained by triangulation of the checkpoints and the true object space three-dimensional coordinates. Table 3 shows the root mean square error of absolute positioning before and after the generalized control constraint adjustment. The deviation range of the initial RPC in the X and Y directions relative to the reference DOM is more than 300 meters, while the deviation range in the Z direction relative to the reference terrain is between 50 and 240 meters. The generalized control constraint adjustment method reduces the deviation in the X and Y directions to within 23 meters and the deviation in the Z direction to within 5 meters by introducing terrain change information of the prior terrain. However, it should be noted that the manually selected checkpoints themselves have certain errors.

[0136] Table 3. Root mean square error of absolute positioning before and after generalized control constraint adjustment (unit: meter)

[0137]

[0138] Using RPC optimized based on the adjustment results, the HRSC / MOLA Blended DEM was used as the base DEM, and orthophotos were generated using an equidistant projection coordinate system. The CTX DOM was resampled to a uniform 6-meter resolution, while the HiRISE DOM was resampled to a 1-meter resolution. Figure 5a to Figure 5i The following figure shows a comparison of the consistency between the DOM before and after adjustment and the image mosaic result of the experimental area. Registration errors often occur at high-frequency terrain features (such as crater rims and curved structures), resulting in pixel offsets between images. The pixel offsets in the experimental area before adjustment were on the order of hundreds of meters, but after generalized control constraint adjustment, they were corrected to sub-pixel levels, demonstrating the effectiveness of the proposed method in resolving image inconsistencies.

[0139] In summary, this scheme addresses the problems of lack of measured control points on the Martian surface and widespread inconsistency between multi-source data. It proposes a joint adjustment method for Mars multi-source orbiter data under generalized control constraints. Using the prior MOLADEM and low-resolution digital orthophoto map (DOM) as control benchmarks, it focuses on breakthroughs in key technologies such as Martian surface point feature extraction and image matching, image square compensation model of superimposed polynomials and cubic spline functions, adjustment model construction based on geometric constraints, and adaptive weighting strategy, to achieve improved geometric positioning accuracy of multi-source orbiter images.

[0140] On the one hand, the large segmentation model is used to extract craters on the Martian surface, and the major-minor axis ratio and area ratio of the crater ellipse fitting are combined to optimize the point set registration method that retains the global and local structure, so as to achieve accurate matching of point features on the Martian surface; on the other hand, an image square deviation compensation model based on cubic splines and affine functions is constructed to effectively consider the linear and nonlinear distortion of the orbiter image; in addition, an adjustment model combining plane weak control constraints, elevation control constraints, image square compensation model, image connection points and virtual control points is constructed.

[0141] By introducing an image space compensation model, affine transformations and cubic spline functions are used to correct linear and nonlinear errors in orbiter images. Tie points, virtual control points, and planar and elevation constraints are integrated to achieve geometric optimization of the images. Craters are extracted and segmented using the SAM model. An improved point set registration algorithm is used to match crater centers and generate reliable planar control points, thus achieving weak planar constraint control. Elevation constraint control is established by combining prior terrain slope information. Furthermore, an adaptive weighting function is introduced to dynamically coordinate various constraints, improving the relative and absolute geometric positioning accuracy of multi-source imagery. The proposed method improves the absolute and relative positioning accuracy of Mars multi-source orbiter images, enabling precise registration of multi-source images under a unified Mars reference datum.

Claims

1. A joint adjustment method for Mars multi-source orbiter images based on generalized control constraints, characterized by: The following steps are involved: S1. Extract plane control points from low-resolution reference DOM and Mars orbiter optical images, and match them to obtain connection points. S2. Based on the fitting residuals of the rational function model of the Mars orbiter image, an image-space compensation model of superimposed polynomials and cubic spline functions is established and the initial compensation parameters are obtained; S3, based on the plane control points, connection points and initial compensation parameters, combined with the plane and elevation control constraints, construct an adjustment model, and adopt an adaptive weighting strategy to solve the adjustment model to obtain the geometric positioning parameters of the image; Step S1 includes the following steps: S11. Preprocessing the Mars orbiter optical image to obtain a preprocessed optical image; S12, extracting connection points between images by least squares template matching for the pre-processed optical image; S13, combining the pre-processed optical image and the low-resolution reference DOM, outputting the crater center through image segmentation, mask segmentation, and crater fitting; The crater centers are then screened, and the matched crater centers are obtained through point set registration to obtain the plane control points.

2. A Mars multi-source orbiter image joint adjustment method based on generalized control constraints according to claim 1, characterized in that: In step S12, for optical images with different illumination, least squares template matching is performed using the Histogram of Oriented Phase Consistency (HOPC) as a similarity measure; For optical images with consistent lighting conditions, the normalized cross correlation (NCC) is used as the similarity measure for least squares template matching.

3. A Mars multi-source orbiter image joint adjustment method based on generalized control constraints according to claim 1, characterized in that: The specific process of step S13 includes: Contrast-limited adaptive histogram equalization and gamma correction are used to perform radiometric enhancement on the pre-processed optical image and the low-resolution reference DOM to strengthen the crater rim. The enhanced image is extracted and segmented using the SAM model; During crater extraction, segmentation masks that do not meet shape requirements are screened out. The area and perimeter of the mask are calculated to evaluate its roundness and ellipticity. When the ratio of perimeter to circumference and the ratio of major axis to minor axis both exceed the threshold of 0.9, the mask is judged to be circular; when the ratio of its major axis to actual area exceeds the threshold of 0.8, the mask is judged to be elliptical. The fitting accuracy of the circle or ellipse is evaluated through edge detection, and only masks with high fitting accuracy and accurate center position are retained. An improved point set registration method that preserves global and local structures is then used to achieve the registration of crater positions between images. First, based on the prior geographic information of the craters, the target centers that lack candidate targets within the distance range are screened out. At the same time, local features are enhanced by constructing a feature descriptor that integrates the aspect ratio, fitting area ratio, and shape context features to ensure that the global and local structures are preserved during the matching process to complete the registration.

4. A Mars multi-source orbiter image joint adjustment method based on generalized control constraints according to claim 1, characterized in that: The specific process of step S2 is: The elevation of the image area is divided into several elevation surfaces. A three-dimensional virtual control grid of the image is established in the image space with set intervals as the image space virtual control points. On this basis, the image coordinates of the virtual control grid are projected onto each elevation surface according to a strict imaging model to obtain the object space virtual control points. The rational function model parameters RPC are calculated by fitting and the geometric relationship between the image space and the object space is expressed using the rational function model. The image space compensation model of linear and nonlinear errors of images is constructed by using superimposed polynomials and cubic spline functions to obtain initial compensation parameters for compensating nonlinear image errors.

5. A Mars multi-source orbiter image joint adjustment method based on generalized control constraints according to claim 4, characterized in that: The expression of the rational function model is: ， in, and Represents the normalized pixel coordinates The offset and scale values, are the normalized latitude, longitude and elevation of the object point, , , and is a third-order polynomial with a total of 20 coefficients, namely the rational function model parameter RPC.

6. A Mars multi-source orbiter image joint adjustment method based on generalized control constraints according to claim 5, characterized in that: The expression of the image space compensation model is: ， Among them, the parameters Represents the affine transformation used to compensate for linear deviation. According to the time variation characteristics caused by posture jitter, the average residual of the image points in the same row is used as the optimal estimate of the RPC nonlinear deviation. The corrected pixel coordinates The calculation is as follows: ， in, is the undetermined parameter of the cubic spline function, which is used to compensate for nonlinear image errors. is the number of connection points.

7. The method for joint adjustment of Mars multi-source orbiter images based on generalized control constraints according to claim 6, wherein: The process of constructing the adjustment model in step S3 includes: The error equation is established and the regional block adjustment is performed. Based on the tie points, virtual control points, planar weak control constraints based on the crater center, terrain elevation constraints, and rational function model parameters RPC obtained by multi-source image matching, the error equation of the joint adjustment model is constructed, which includes the following four observation equations: ， in, For the connection point, is the elevation control point, is a virtual control point, are plane control points, is the observation equation of the connection point, is the observation equation of the elevation control constraint, is the observation equation of the plane control constraint, is the observation equation of the virtual control point, is the correction number of the adjustment parameter, For the unknown ground coordinates of the connection points, the matrix and are the error equation coefficient matrices, is a constant term, For the corresponding weight, for the error equation, construct the normal equation, and use the least squares method to fit the object coordinates of the most approximate connection points; Then, terrain constraints based on the prior DEM are introduced, and elevation control is achieved by minimizing the sum of squares of the distances from the connection points to the DEM surface, which corresponds to the second observation equation Elevation error equation The expression is as follows: ， in, is the elevation of the connection point, is the geographic plane coordinate, for The elevation value obtained by interpolation at and are the slopes of DEM in latitude and longitude, respectively. is the correction number of the ground coordinates of the connection point; A plane weak control constraint is constructed to incorporate terrain change information near the plane control point into the plane control constraint. The object space 3D coordinates of the control point are adaptively adjusted to achieve automatic registration between the image and the crater center in the DOM. The difference between the pixel coordinates of the plane control point and the corresponding 3D coordinates in the reference map is minimized by the following formula: ， in, are the pixel coordinates of the crater center in the orbiter image, and The pixel coordinates of the center of the crater are obtained by reprojecting the object space coordinates in the reference DOM and DEM onto the image, which is consistent with the third observation equation. The corresponding plane error equation and The expression is as follows: ， ， ， in, as well as , and Get the derivatives of the image coordinates with respect to latitude, longitude, and elevation for reprojection, are the pixel coordinates of the plane control points, is the difference between the image coordinates calculated by reprojecting the object space coordinates of the plane control points and the corresponding actual image coordinates; Reintroducing virtual control points To effectively reduce the degrees of freedom of the adjustment model, Strategically selected by pre-defined thresholds and grid sizes, and introduced as weighted observations into the fourth observation equation middle.

8. The method for joint adjustment of Mars multi-source orbiter images based on generalized control constraints according to claim 7, wherein: The process of adopting the adaptive weighting strategy to solve the adjustment model in step S3 includes: Based on prior matching accuracy , defines the weight of the connection point for: ， Taking the prior terrain as the control information, and the errors and point inconsistencies caused by the terrain as the "gross errors" in the control information, in order to quantitatively reflect the impact of the terrain on the accuracy, the weight function of the elevation control constraint is defined as follows: ， in, is the image resolution, is the elevation difference, is the average elevation difference of the dataset, is the standard deviation of the elevation difference. When the elevation difference is less than When the height difference exceeds three times, the weight is determined by the image resolution and the standard deviation of the height difference. When , the weight gradually approaches zero; Calculate the scale factor for each adjustment area separately and ,in is the number of connection points, is the number of plane control points, is the number of virtual control points, considering the crater radius and the fitted standard deviation , the weight function of the plane control constraint is defined as follows: ， ， in, is the plane difference, is the average of the plane differences, is the standard deviation of the plane differences, is the normalized radius factor, is the normalized fitting factor, the smoothing function along with Approaching the set threshold Gradually reduce the weight to zero, the steepness of the transition curve changes from constant Control, when the standard deviation is lower than When the value is higher than When , the weight decreases more slowly; A dynamic weighting function is established to alleviate the imbalance between the number of virtual control points. The unknown ground coordinates of the connection points are calculated based on the least squares method to solve the refined geometric positioning parameters of each image.

9. The method for joint adjustment of Mars multi-source orbiter images based on generalized control constraints according to claim 8, characterized in that: The expression of the dynamic weighting function is: ， in, Indicates the number of iterations.

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