Plenty of effort has been devoted to solving the nonlinear radiation distortions (NRDs) in planetary image matching. The mainstream solutions convert multimodal images into “single” modal images, which requires building the intermediate modalities of images. Phase congruency (PC) features have been widely used to construct intermediate modalities due to their excellent structure extraction capabilities and have proven their effectiveness on Earth remote sensing images. However, when dealing with large-scale planetary remote sensing images (PRSIs), traditional PC features constructed based on the log-Gabor filter take considerable time, counterproductive to global topographic mapping. To address the efficiency issue, this work proposes a fast planetary image-matching method based on efficient PC-based feature transform (EPCFT). Specifically, we introduce a method to calculate PC using Gaussian first- and second-order derivatives, called efficient PC (EPC). Different from the log-Gabor filter, which is sensitive to structures in a single direction,$\rm EPC$uses circularly symmetric filters to equally process changes in all directions. The experiments with 100 image pairs show that compared with other methods, the efficiency of our method is nearly doubled without loss of accuracy.

The accurate matching of planetary remote sensing images (PRSIs) is the premise of accurate planetary terrain mapping. However, PRSIs often lack apparent man-made structures such as buildings or roads, leading to difficulties in feature description. In addition, the PRSIs collected by different sensors are affected by the imaging mechanism and the solar illumination, and there are obvious nonlinear radiation differences (NRDs). These problems make the matching of PRSIs difficult. To address the above issues, this article proposes a PRSI matching method based on optimized bandpass filtering and local topological prior, divided into two stages: coarse matching and fine matching. In the coarse matching stage, we first use the bandpass filtering to calculate the phase congruency (PC). Then, the feature block descriptors are constructed, and the local topology consensus is used to achieve the coarse alignment of feature blocks. Finally, we extract the point features and use the matching results of block features to narrow the matching range of point features. Based on the coarse matching results, the precision and reliability of the results are further improved through fine matching. The experimental results achieved with a PRSI dataset with 75 image pairs demonstrate that our method is superior to other recent methods, the matching accuracy of the proposed method is improved by more than 2.367 pixels, and the success rate is improved by over 22.667%. The source code will be publicly available athttps://github.com/WGY-RS/OFLP.

Deep learning-based feature matching methods have been actively explored for satellite image bundle block adjustment, leveraging their inherent robustness to large geometric distortions and radiometric differences—key challenges in multi-source optical image processing. However, their practical utility remains limited by two critical bottlenecks: poor adaptability to extreme scale variations and prohibitive computational costs for large-format satellite data. To address these limitations, we propose a learning-based feature representation method enhanced by nonlinear diffusion filtering, with two targeted innovations: (1) Nonlinear diffusion filtering with terrain-adaptive parameters is integrated into a novel image tiling strategy, which preserves local feature integrity while enabling consistent correspondence across heterogeneous satellite data sources; (2) A top-down pyramid construction mechanism that incorporates local continuity constraints and an adaptive matching strategy selection protocol optimizes scale space exploration efficiency while safeguarding matching quality. Experiments on Earth observation and Martian image datasets validate the method’s superiority: it achieves the highest matching success rate (97.25%) and the highest BBA accuracy(1.65 pixel) among competing approaches, alongside competitive efficiency. This performance advantage is particularly pronounced under extreme scale differences and challenging imaging conditions, confirming its suitability for high-precision remote sensing applications.

During the Orbital B Phase mission of the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx), the OSIRIS-REx Laser Altimeter (OLA) instrument scanned asteroid Bennu and obtained hundreds of point cloud frames, making a meticulous 3-D surface modeling of Bennu realizable. However, these acquired point clouds suffer from problems such as inaccurate pose information, low overlap ratios, uneven densities, and outliers. To create a precise 3-D shape model via topographical mapping using OLA data, we present an optimized connection graph-guided global bundle block adjustment (BA) method with variable weights capable of constructing a complete 3-D model of the asteroid via a straightforward global adjustment of offsets. Specifically, by using an improved Fast Double-channel Aggregated Feature Transform (iFDAFT), 3-D keypoint extraction and their correspondences can be determined with a subpixel level of matching accuracy. Then, a graph of the shortest path algorithm is constructed to establish the optimized connections of all the OLA point clouds. Afterward, the global bundle BA method with variable weights is introduced to minimize the keypoint matches simultaneously, reducing the offsets between the overlapping point clouds. Finally, the shape model of asteroid Bennu was constructed using the finely adjusted point clouds, with RMSE as small as 0.0743 m compared with the reference shape model, reaching approximately the limit of the instrument range errors (i.e., 3 cm) of observation data. Results of the case study on asteroid Bennu demonstrated that the proposed method could improve the accuracy of point cloud registration and meet the application requirements.

Matching multimodal remote sensing images (MRSIs) is a challenging task. Due to significant nonlinear radiation differences (NRDs), traditional image-matching methods cannot achieve satisfactory results. This article shows that structural information can get more robust matching results compared with texture information (i.e., gradient features) from images. In order to better explore the structural information of images, this article proposes an MRSI matching method using structure saliency features, called weighted structure saliency feature (WSSF). Two strategies are investigated and integrated into WSSF to improve the matching performance. The scale space is constructed based on the pointwise shape-adaptive texture scale filtering, which can better retain the structure features, and the second-order Gaussian steerable filtering, edge confidence map, and phase features are combined to establish the structural saliency map combined with second-order Gaussian steerable filtering, which is much more robust to NRD than traditional gradient map. The performance of the proposed method was evaluated on a total of 120 image pairs from two MRSI datasets and compared with the state-of-the-art matching methods, including the histogram of the orientation of weighted phase (HOWP), locally normalized image feature transform (LNIFT), co-occurrence filter space matching (CoFSM), radiation-variation insensitive feature transform (RIFT), local phase sharpness orientation (LPSO), and position-scale-orientation scale-invariant feature transform (SIFT) (PSO-SIFT). The experimental results indicate that WSSF obtains satisfactory and reliable results in terms of success rate (SR) and matching accuracy. Compared with the above six methods, the matching accuracy of WSSF is improved by more than 20.275%, and the SR is improved by over 5.833%. The source code will be publicly available athttps://github.com/WGY-RS/WSSF.