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.

In planetary environments, conducting autonomous exploration tasks requires rovers to autonomously navigate the scene and achieve a detailed understanding of the terrain. Vision-based simultaneous localization and mapping (SLAM), which utilizes compact and low-power visual sensors for autonomous exploration, offers significant advantages in hardware deployment. While several methods have been proposed to apply visual navigation in planetary scenarios, they often rely on aerial imagery from orbiters and high-resolution DEMs for assistance. Additionally, accurate camera poses are typically required for dense matching during scene reconstruction, and the inability to perform loop closure significantly limits the performance of visual SLAM. Here, we propose a monocular visual SLAM approach combined with an adjusted neural radiance field for autonomous navigation and 3D reconstruction in planetary environments. Our approach solely relies on visual images as input and leverages the powerful learning capabilities of neural radiance fields to adapt to unseen scenes while simultaneously regressing both camera poses and scene representations. The estimated depth maps and poses can be further used for 3D reconstruction, assisting planetary exploration missions. The proposed method was tested on the Devon and MADMAX datasets that simulate planetary environments and achieved remarkable results. Even under the fixed rover navigation perspective, our pose estimation accuracy outperforms classical visual SLAM and other deep learning-based SLAM methods. Additionally, our novel view synthesis results exhibit quality comparable to those in terrestrial scenes. Comparisons with MVS techniques in terms of 3D reconstruction demonstrate that our approach recovers finer surface details. We also applied our method to the Perseverance rover dataset and achieved satisfactory positioning and reconstruction results in a real Martian environment, proving the practical feasibility of our method.

Accurate simulation of subsurface physical temperatures in lunar south polar craters is essential for thermal environment analysis in landing site selection. In this study, we developed an improved method for simulating large-scale subsurface temperature by integrating multi-source datasets, including Lunar Reconnaissance Orbiter (LRO) DEM, LRO Diviner infrared brightness temperature (TB), and Chang’E-2 microwave TB. The proposed method accounts for differences in heat sources between non-permanently shaded regions (non-PSR) and permanently shaded regions (PSR) craters, applying effective solar irradiance with terrain effect for non-PSR craters, and calibrated infrared TB with emissivity effect for PSR craters. Additionally, we incorporated key model parameters, including an annual model period and updated thermal conductivity, into a one-dimensional (1-D) heat transfer model. This method was applied to simulate subsurface temperatures (0–2 m depth) in lunar south polar craters, validated by the microwave radiative transfer model and observed microwave TB data. The simulation results indicate that temperatures near the lunar south polar are generally lower than those in the 80°-85°S latitude range. These temperature profiles can be applied to quantitatively estimate the detection depths of heat flow and buried water ice. Furthermore, the model period considering seasonality shows a stronger impact on temperature simulations than other model parameters — altering temperatures by 20–40 K in non-PSR and by 20–30 K in PSR. Our method and findings provide valuable insights for future scientific exploration of the lunar south pole region and contribute to a better understanding of subsurface thermal evolution.

Digital elevation models (DEMs) play a crucial role in scientific research and exploration mission of the lunar south pole regions, as they offer vital topographic and morphological information of terrain surfaces. Surface points processed from various remote sensors provide a discrete but accurate measurements of lunar terrains. However, reconstructing high-resolution terrains from surface points poses significant challenges for neural networks primarily due to the lack of grid connectivity inherent in unordered surface points. Therefore, this study develops a point-based terrain reconstruction framework that incorporates learnable neural shape priors to better capture complex terrain geometry. To model structural characteristics inherent in three-dimensional (3-D) surface points, signed distance field (SDF) is introduced as a continuous representation of lunar surfaces. Moreover, we train a 3-D point-based encoder–decoder network that allows terrain modeling from unstructured surface points with encoded planar features. Additionally, a dynamic planar features strategy is proposed to optimize the framework’s performance to the characteristics of terrain flatness. Experiments were conducted on photoclinometry DEMs of the lunar polar region to demonstrate the advantages of our proposed framework. The qualitative and quantitative results highlight that our framework reconstructs accurate terrain geometry, outperforming traditional methods in terms of elevation, slope, aspect and roughness. Furthermore, the model exhibits a generalization ability when applied to both Lunar Orbiter Laser Altimeter (LOLA) measurements and unseen regions, underscoring its effectiveness in lunar exploration tasks.

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.

Mapping of planetary surface using stereo orbital imagery is a basic task of planetary exploration. As the number of orbiter images increases, excessive images will lead to unnecessary time costs and accuracy loss during the photogrammetric mapping process. However, existing studies focus more on the factors that affect the mapping accuracy and often ignores the redundancy and distribution rationality of images in the mapping process. Therefore, optimal data selection from extremely redundant orbiter images has become a challenging and urgent issue for planetary mapping. This paper proposes an effective optimal selection method of off-track stereo pairs, which extends the selection operation from a local to a global perspective, and reduces the redundancy of image pair subset covering the region of interest (ROI) based on multi-constraints such as quality constraints and global distribution constraints. Specifically, the ROI is rasterized and gridded, and the optimal and candidate stereo pairs are selected for each grid based on the quantified quality constraints. By combining global distribution constraints and consistency constraints between adjacent stereo pairs, an optimization model is constructed and the optimal subset of stereo pairs is determined using belief propagation algorithm. Experiments conducted on the selected ROIs of Moon and Mars indicate that our method significantly reduces data redundancy while ensuring the quality and rational distribution of stereo image pairs. Qualitative and quantitative evaluations demonstrate that the stereo pairs obtained using the proposed method perform exceptionally well in terms of intersection angles, redundancy rates, and other factors, showing overall superior performance compared to other methods. Additionally, the stereo pairs obtained by the proposed method were employed for photogrammetric mapping within the ROI, and the results validate the mapping products also exhibit satisfactory accuracy.

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.

The 3D reconstruction of the lunar surface is a fundamental task, with stereo matching algorithms play a crucial role. The cornerstone of stereo matching is the computation of the matching cost, a step that significantly influences the ultimate result. In this paper, an improved census cost computation method is proposed and integrated in the Semi-Global Matching(SGM), which incorporates two-bit string to encode the relationship between pixels and ternary operator combined with an adaptive threshold. In this way, the matching robustness at depth discontinuities can be effectively improved and is particularly suitable for the case of multiple craters on the lunar surface. Experimental results show that the proposed method can greatly improve the matching results at lunar craters.

A rational function model (RFM) has long been a versatile tool in image block adjustment when building the global control network in lunar topographic mapping. By unifying the physical imaging model with mathematical expressions, the RFM facilitates using multisource images from various missions. However, the scarcity of ground control points (GCPs) on the lunar surface and the imaging void of Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) limits the application of RFM in large-scale mapping tasks. This work proposed a time-based RFM generation approach for strip-based image block adjustment using merely sparse control points. The proposed time-based RFM integrates separated image pieces in the same orbit into a cohesive whole for strip-based block adjustment, requiring merely sparse control points when mapping and geopositioning all the image pieces. Besides, we employ the elevation control information extracted from Lunar Orbit Laser Altimeter (LOLA) data in the strip-based block adjustment. Experiment results indicate that under the same sparse control conditions, the elevation accuracy using strip-based adjustment is improved by an average of 12.8% compared with single-based adjustment, and our method achieves a similar adjustment accuracy using sparse elevation controls as that in the densely controlled single-based block adjustment.

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.

Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds. However, the finding of reliable correspondence relies on establishing a robust and discriminative description of elements and the correct matching of corresponding elements. In this letter, we develop a coarse-to-fine registration strategy, which utilizes rotation-invariant features in frequency domain and a new graph matching (GM) method for iteratively searching correspondence. In the GM method, the similarity of both nodes and edges in the Euclidean and feature space is formulated to construct the optimization function. The proposed strategy is evaluated using two benchmark datasets and compared with several state-of-the-art methods. Regarding the experimental results, our proposed method can achieve a fine registration with rotation errors of less than 0.2° and translation errors of less than 0.1 m.

The semantic interpretation using point clouds, especially regarding light detection and ranging (LiDAR) point cloud classification, has attracted a growing interest in the fields of photogrammetry, remote sensing, and computer vision. In this letter, we aim at tackling a general and typical feature learning problem in 3-D point cloud classification- how to represent geometric features by structurally considering a point and its surroundings in a more effective and discriminative fashion? Recently, enormous efforts have been made to design the geometric features, yet it is less investigated to fully explore the potentials of the features. For that, there have been many filter-based studies proposed by selecting a subset of the whole feature space for better representing the local geometry structure. However, such a hard-threshold selection strategy inevitably suffers from information loss. In addition, the construction of the geometric features is relatively sensitive to the size of the neighborhood. To this end, we propose to extract multi-scaled feature representations and locally embed them into a low-dimensional and robust subspace where a more compact representation with the intrinsic structure preservation of the data is expected to be obtained, thereby further yielding a better classification performance. In our case, we apply a popular manifold learning approach, that is, locality-preserving projections, for the task of learning low-dimensional embedding. Experimental results conducted on one LiDAR point cloud data set provided by the 2018 IEEE Data Fusion Contest demonstrate the effectiveness of the proposed method in comparison with several commonly used state-of-the-art baselines.

Light Detection and Ranging (LiDAR) techniques is an efficient way of obtaining 3D information of complex urban scenes. However, automatically and efficiently interpreting acquired 3D points is still a challenging task. For achieving an excellent semantic interpretation of point clouds, the extraction of distinctive and reliable geometric features often plays a vital role. In this paper, we propose a method generating features from the local vicinity of different sizes and combine them for a better feature representation. To evaluate the proposed method, experiments were conducted using Li-DAR point cloud dataset and compared with that using single scale feature extraction methods.