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Landing point localization is of great significance to the lunar exploration engineering and scientific missions. Vision-based landing point localization methods have successfully been utilized in Chang'e series missions. The issues in landing point visual localization task containing low-resolution reference maps, illumination changes between descent images and maps, and low automation of the localization workflow still need to be solved. In this article, a high-precision and automatic landing point visual localization method with high-resolution map generation is proposed, including initial localization of the first frame, hybrid fine matching, landing point propagation in descent sequence images, and absolute position estimation for landing point. High-resolution digital elevation model and digital orthophoto map (DOM) are generated from Lunar Reconnaissance Orbiter Camera Narrow Angle Camera images and SLDEM2015 data. Phase-based image matching method is adopted for initial localization and matching between descent image and reference map to enhance the illumination robustness. The performance of our method is validated using the descent sequence images from Chang'e series missions. For Chang'e-6 lander, the estimated landing point coordinates are ($-$153.9870$^{\circ }$ $\pm$ 0.00002$^{\circ }$, $-$41.6378$^{\circ }$ $\pm$0.00001$^{\circ }$, $-$5256.962 m $\pm$ 0.0041 m). Compared with the manually measured lander coordinates, the deviation of landing point position is less than 1 pixel in DOM.

Matching planetary remote sensing images (PRSI) is essential for deep space exploration. Through matching optical images collected by different probes, the terrain of planets can be accurately mapped; however, PRSI lacks surface texture information and exhibits noticeable nonlinear radiation differences, e.g., illumination differences. It is not easy to achieve satisfactory results through traditional matching methods. In order to cope with the above problems, a new PRSI matching method has been proposed. It can extract low–high frequency features by building double-frequency scale space. In addition, we also use the nonmaximum suppression strategy for rejecting overlapped feature points, which reduces the time consumption and improves the matching accuracy. The experimental results show that the proposed method can effectively match PRSI and be superior to comparable methods.

Mismatch removal is a crucial step in multiview matching of lunar orbiter images. This process involves complex challenges like terrain-induced distortion and nonunique geometric structures due to repetitive textures. Traditional methods, whether global constraints or local constraints, fall short in adequately addressing these issues in orbiter imagery. Therefore, this article proposes an effective method for mismatch removal of orbiter images based on global constraint and local geometry preservation combined with the imaging model. In this method, a clean neighborhood of each matching point based on the characteristic of centralized distribution of the back-projection residuals globally is constructed. In the local region, we define a local minimum geometric polygon consisting of the center feature point and its three neighbors, and combine the similarity of the back-projection difference vectors with the affine invariance of the polygon to distinguish the correct matches and mismatches by measuring the degree of local geometry preservation. A series of experiments encompassing parameter sensitivity analysis, comparison studies and ablation experiments were conducted on the lunar reconnaissance orbiter image datasets to demonstrate the effectiveness and reliability of the proposed method. The results indicate that our method exhibits a notable insensitivity to parameter variations, and outperforms other advanced methods in both qualitative and quantitative evaluations. Moreover, a large-scale orbiter images multiview matching tie points extraction framework is extended based on the proposed mismatch removal method, which can achieve better results than commonly used photogrammetric software in terms of the number and accuracy of tie points.

Multimodal images refer to images obtained by different sensors, and there are serious nonlinear radiation differences (NRDs) between multimodal images for photos of the same object. Traditional multimodal image matching methods cannot achieve satisfactory results in most cases. In order to better solve the NRD in multimodal image matching, as well as the rotation and scale problems, we propose a two-step matching method based on co-occurrence scale space combined with the second-order Gaussian steerable filter (G-CoFTM). We first use the second-order Gaussian steerable filter and co-occurrence filter to construct the image’s scale space to preserve the image’s edge and detail features. Secondly, we use the second-order gradient direction to calculate the images’ principal direction, and describe the images’ feature points through improved GLOH descriptors. Finally, after obtaining the rough matching results, the optimized 3DPC descriptors are used for template matching to complete the fine matching of the images. We validate our proposed G-CoFTM method on eight different types of multimodal datasets and compare it with five state-of-the-art methods: PSO-SIFT, CoFSM, RIFT, HAPCG, and LPSO. Experimental results show that our proposed method has obvious advantages in matching success rate (SR) and the number of correct matches (NCM). On eight different types of datasets, compared with CoFSM, RIFT, HAPCG, and LPSO, the mean SRs of G-CoFSM are 17.5%, 6.187%, 30.462%, and 32.21%, respectively, and the mean NCMs are 5.322, 11.503, 8.607, and 16.429 times those of the above four methods.

The registration of panoramic images and mobile light detection and ranging (LiDAR) data is quite challenging because different imaging mechanisms and viewing angle differences generate significant geometric and radiation distortions between the two multimodal data sources. To address this problem, we propose a registration method for panoramic images and mobile LiDAR data based on the hybrid geometric structure index feature of phase. We use the initial GPS/IMU to transform the mobile LiDAR data into an intensity map and align the two images to complete registration. Firstly, a novel feature descriptor called a hybrid geometric structure index of phase (HGIFP) is built to capture the structural information of the images. Then, a set of corresponding feature points is obtained from the two images using the constructed feature descriptor combined with a robust false-match elimination algorithm. The average pixel distance of the corresponding feature points is used as the error function. Finally, in order to complete the accurate registration of the mobile LiDAR data and panoramic images and improve computational efficiency, we propose the assumption of local motion invariance of 3D–2D corresponding feature points and minimize the error function through multiple reprojections to achieve the best registration parameters. The experimental results show that the method in this paper can complete the registration of panoramic images and the mobile LiDAR data under a rotation error within 12° and a translation error within 2 m. After registration, the average error of rotation is about 0.15°, and the average error of translation is about 1.27 cm. Moreover, it achieves a registration accuracy of less than 3 pixels in all cases, which outperforms the current five state-of-the-art methods, demonstrating its superior registration performance.

Due to the uncertainty in output caused by environmental changes, significant discrepancies are expected between the surface flow velocities predicted using deep learning methods and the instantaneous flow velocities. In this paper, a two-stage deep learning flow velocity measurement algorithm is proposed. During the external calibration process, the upper and lower frames of the recorded water flow video are cyclically traversed to acquire predicted flow velocity values using the deep learning velocity measurement algorithm. Meanwhile, the pixel displacement is obtained using the sparse optical flow tracking method and then post-processed to derive the velocity calibration value and pixel calibration value. During the detection process, the deep learning-predicted flow velocity is internally calibrated using the velocity calibration value and the pixel calibration value to adapt to changes in water flows. Compared with the pre-improved algorithm, the method achieves the minimum root mean square error in five different flow velocity videos and maintains high accuracy when the flow velocity changes rapidly. The obtained results are very promising and can help improve the reliability of video flow rate assessment algorithms. HIGHLIGHTS New methods are needed to ensure the instability of the video-based surface velocity prediction due to environmental changes.; Avoid direct measurement of high velocity, using the slow to fast process to indirectly get accurate high velocity.; We provide a river data set for deep learning model training, which is convenient for researchers interested in this to expand their research.;