The mineral composition of lunar regolith influences brightness temperature (TB) as observed by the microwave radiometer (MRM); however, the large-scale spatial relationship between TB and mineral abundance has yet to be sufficiently revealed. This study aims to quantify the impact of specific mineral abundances (plagioclase and ilmenite) on TB distribution, using MRM 37-GHz data from Chang’E-2 and mineral abundance products from Kaguya. We applied hour angle correction and latitude normalization to produce high-accuracy TB maps and developed a self-adaptive moving-window method to remove strip noise to produce higher precision mineral abundance maps. Using these two types of maps, we performed comprehensive large-scale spatial analysis of TB and mineral abundance using a bivariate spatial autocorrelation model and a geographically weighted regression (GWR) approach considering spatial similarity and heterogeneity, respectively. The bivariate analysis indicates a negative spatial correlation between TB and plagioclase abundance, while a positive spatial correlation between TB and ilmenite abundance. In addition, bivariate anomalies, including both hot and cold spots of diurnal TB amplitude (i.e., noon minus nighttime), were identified through the simultaneous consideration of TB and mineral abundance. The GWR analysis reveals regional variations in the impact of mineral abundances on diurnal TB amplitudes. These correlations can be attributed to the spatial distributions and variations in TB, which arise from the unique dielectric and thermal properties of minerals across distinct regions on the Moon. These findings contribute to a better understanding of subsurface thermal behavior and regimes, enhancing our comprehension of lunar evolution.

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 positioning accuracy of synthetic aperture radar (SAR) images is affected by factors, such as satellite platform instability, aging of on-board instruments, and environmental changes. Geometric calibration is a commonly employed and cost-effective method to enhance the positioning accuracy of SAR images. The classical point-based geometric calibration (PB-GC) model, however, only utilizes the location of ground control points (GCPs) and does not fully exploit the spatial relationships among the GCPs. This study introduces a high-precision geometric calibration method that builds upon the classical model for calibrating SAR imaging systems. This method incorporates the Co-Line-GC and Co-Circle-GC models, where the former uses GCPs distributed on a line while the latter uses GCPs distributed on a circle. The results reveal that, compared to the classical model, our approach enhances the positioning accuracy of Gaofen-3 and Sentinel-1A SAR images by approximately 2 m in eastern China, achieving a mean positioning accuracy of 3.02 m. In terms of calibration performance, a comparison between postcalibrated and precalibrated images indicates that the images are shifted, not distorted, and a better match of the same features between different scenes in the image mosaic is observed after calibration. The improved positioning accuracy of SAR images significantly contributes to global remote sensing mapping, land use change monitoring, and ground target detection applications.

The investigation of small lunar craters holds scientific and engineering significance. This paper presents a novel method for detecting small lunar craters. It consists of three stages: seed detection, candidate crater detection, and crater evaluation. Firstly, crater seeds are identified through morphological operations as pixels with the highest local gradient and specific gradient direction. Secondly, the optimal scale for each seed is estimated based on the maximum response of the established phase congruency maximum moment (PCMM) scale space. For detecting very small craters, a crater detector called statistical morphological constraint path-sets (SMPS), which leverages image spatial domain features, is proposed. It configures the image as a weighted directed graph, using path-sets centered on seeds to flexibly detect highlights and shadow regions of craters. For detecting craters with larger optimal scale, another crater detector named structural consistency constrained multi-paths (SCMP) is proposed, utilizing the frequency phase features. The core idea of SCMP is to configure the phase feature type (PFT) map with the optimal scale as a directed graph. Centered on the seed, the multi-path operator is designed to detect craters. Unsupervised discriminative correlation filters (UDCFs) are trained with HOG features from images or PFT maps to validate candidate craters. The results indicate that for images with a resolution of 0.5-2m/pixel, the proposed method demonstrates good detection performance for small craters with diameters of less than 5 m, 5-10 m, and greater than 10 m, with an average detection rate of 0.89, 0.91, and 0.92, respectively.

Ocean surveying is of great significance to mankind’s development and utilization of the ocean. Island and reef area surveying is an important part of ocean surveying and mapping. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) has been proven to have a certain bathymetric capability. However, the precise extraction of seafloor signal photons in these regions remains a challenge. This study introduces a method for extracting seafloor photons that is water depth adaptive and works at various depths. In addition, we propose a method to evaluate ICESat-2 seafloor signal photons by underwater light-beam propagation and noise modeling, using the decision tree method to classify signal photons into high-, medium-, and low-confidence levels. The results indicate that the method exhibits better signal continuity, better slope adaptability, and better SNR adaptability in seafloor signal photon detection, and remain more surface object signal photons in island signal photon detection thanAVEBMmethod. The high-, medium-, and low-confidence seafloor signals exhibit consistencies (R2) of 0.9954, 0.9926, and 0.9874, respectively. The root-mean-square errors (RMSEs) are 0.49 m, 0.66 m, and 0.93 m, and the mean absolute errors (MAEs) are 0.24 m, 0.44 m, and 0.86 m, correspondingly. Higher-confidence photons perform significantly better than lower-confidence photons. The confidence evaluation of seafloor photons will provide an important reference for users, and will lay the foundation for further research into the use of ICESat-2 for offshore bathymetry.

A full-waveform laser altimeter measures the round-trip time-of-flight of the laser pulse to estimate the range between the altimeter and the target, while the vertical distribution information of the terrain within the laser footprint is recorded in the full-waveform data. However, the waveform width is broadened by the target surface slope and roughness. In previous studies, the relationship between the laser altimetry waveform width and the target surface slope and roughness has been modeled based on the assumption that the laser footprint on the Earth’s surface is a circle. In this letter, based on the previous model, we propose an improved within-footprint slope estimation model by combining the shape and orientation information of the elliptic laser footprint, which further improves the accuracy of the model. The validation and accuracy assessment were performed using a high-resolution digital elevation model (DEM) of the Antarctic ice sheet. The results show that the slopes within the footprint calculated using the improved model are close to the slopes extracted from the DEM, with the mean value of the slope bias being 0.18°, standard deviation (STD) being 1.36° and root-mean-square error being 1.46°.

As the accuracy of satellite laser altimetry is susceptible to real-time atmospheric conditions, with-in footprint topography fluctuation, and detector noise, etc., we proposed a method by comprehensively analyzing the laser ranging error and evaluation labels to extract high-accuracy elevation control points that is suitable for satellite imagery based topographic mapping applications. Using the ICESat laser altimetry data, a global high accuracy laser altimetry dataset including more than 60 million control points, based on the laser altimetry ranging error model and waveform quality analysis is proposed by the paper. For land areas, except for areas of water, snow/ice, and polar ice sheets, the dataset can provide the elevation control points for worldwide satellite topographic mapping using high spatial resolution imageries or other science researches that depend on accurate earth’s elevation information. We further used airborne lidar data from six study areas around the world to carefully validate the dataset’s accuracy. The results showed that, this dataset can meet the accuracy requirement of global mapping using high spatial resolution satellite imageries in terrains with a slope below 25°. Compared to the raw dataset, the proportion of footprint elevations that conform to the accuracy standard (0.5m@ slope<2°, 1.5m@ 2°≤slope<6° and 3m@ 6°≤slope<25°) is increased from 68.24%, 59.97% and 26.52% to 87.58%, 90.04% and 83.91% respectively. This method can assure that its extracted results’ accuracy is either very close to or better than that obtained by the methods proposed in relevant studies, with a much larger number of laser footprints have been reserved.

With more detailed spatial information being represented in very-high-resolution (VHR) remote sensing images, stringent requirements are imposed on accurate image classification. Due to the diverse land-objects with intraclass variation and interclass similarity, efficient and fine classification of VHR images especially in complex scenes is challenging. Even for some popular deep learning (DL) frameworks, geometric details of land-object may be lost in deep feature levels, so it is difficult to maintain the highly-detailed spatial information (e.g., edges, small objects) only relying on the last high-level layer. Moreover, many of the newly developed DL methods require massive well-labeled samples, which inevitably deteriorates the model generalization ability under the few-shot learning. Therefore, in this paper, a lightweight shallow-to-deep feature fusion network (SDF2N) is proposed for VHR image classification, where the traditional machine learning (ML) and DL schemes are integrated to learn rich and representative information to improve the classification accuracy. In particular, the shallow spectral-spatial features are first extracted, and then a novel triple-stage fusion (TSF) module is designed to learn the saliency and discriminative information at different levels for classification. The TSF module includes three feature fusion stages, i.e., low-level spectral-spatial feature fusion, middle-level multi-scale feature fusion, and high-level multi-layer feature fusion. The proposed SDF2N takes advantages of the shallow-to-deep features, which can extract representative and complementary information of crossing layers. It is important to note that even with limited training samples, the SDF2N still can achieve satisfying classification performance. Experimental results obtained on three real VHR remote sensing data sets including two multispectral and one airborne hyperspectral images covering complex urban scenarios confirm the effectiveness of the proposed approach compared with the state-of-the-art methods.

Array interferometric synthetic aperture radar (Array InSAR) has a 3-D resolution capability and solves the layover problem in interferometric SAR (InSAR) by arranging multiple antennas in the cross-orbit direction. Airborne Array InSAR point clouds are obtained from two scans for complete building information in urban areas, resulting in very low overlapping point cloud. The existing methods are difficult to extract the identical features for the registration of Array InSAR point clouds. To this end, a robust registration approach Array InSAR point clouds in urban areas is proposed in this study. The main contribution of this article is raising the theoretically optimal transformation for achieving point cloud registration, considering the constraint from parallel facades of a certain building. Point density estimation is adopted to retain building facade points for initial registration. The facade pairs of a specific building are then matched and divided into two categories by judging whether one contains the concave–convex features or not, for performing rotation rectification and fine shift fixation, respectively. Experimental results of both simulated and real data validate the feasibility and reliability of our approach. For the simulated data, the results reach an average rotation error of about 0.01° and an average translation error of less than 0.8 m. For the real data, two evaluation criteria are designed for the lack of reference data. The results reach an average of 0.4° of the defined angle difference and less 0.8-m distance difference from the source facades center to the normal extension of the target facades.

Crater detection from planetary images is a challenging issue due to the complicated variations in geometry shape, illumination, and scale. An automatic crater detection algorithm (CDA) that is robust to these factors is, therefore, necessary. In this article, a novel automatic CDA that is robust to these factors is proposed to detect the multiscale craters of the Moon. The proposed method consists of two main steps: 1) in the hypothesis generation (HG) step, a novel feature operator called the path-profile, which is constructed based on the self-defined adjacency graph and a path descriptor, is presented to derive the highlight-shadow feature of craters for detecting candidate craters. 2) In the hypothesis verification (HV) step, based on the idea of anomaly detection, the isolation forest algorithm which is an unsupervised learning anomaly detection method is applied to eliminate falsely detected craters. Lunar Reconnaissance Orbiter Camera Wide Angle Camera and Narrow Angle Camera images and Chang’E-4 landing camera images were used to test the accuracy and robustness of the proposed method. The experimental results indicate that: on average, the accuracy of the detection result of the HG step is about 90%, and the HV step can further improve this by 3%–4%. The proposed method is a reliable way to detect multiscale lunar craters for various resolutions images with diameters ranging from five pixels to hundreds of pixels, and it is robust to the different terrains and illumination conditions on the Moon.

ZY-3 is the first civilian mapping satellite capable of stereo observation in China. This study systematically compares and analyzes block bundle adjustment models for Chinese ZY-3 satellite imagery. The experimental results revealed that: (a) there is no significant difference between the results of RSM-based (Rigorous Sensor Model based) and that of the RFM-based (Rational Function Model based) method for direct forward intersection. (b) it is reasonable to replace the RSM-based method using the RFM-based method when there are approriate control points. (c) the distribution of control points has a certain influence on the positioning results, especially for the RSM-based method. The use of shift model in image space can greatly improve the positioning accuracy when one control point is located in image center, and the systematic errors in initial RPCs of Chinese ZY-3 satellite are mainly translation errors.

The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness.

High-resolution satellite images (HRSIs) obtained from linear array charge-coupled device sensors always suffer from geometric instability in the presence of attitude jitter. Therefore, detection and compensation of spacecraft attitude jitter in both the cross-track and along-track directions are crucial to improve the geometric accuracy of HRSIs. A number of reports have been made on the detection and estimation of cross-track attitude jitter. However, the detection of the attitude jitter in the along-track direction is more complicated due to the impact of topographic change. This paper presents a novel approach to achieve accurate estimation of the along-track attitude jitter by eliminating the influence of topographic information based on the back-projection residuals of three-line-array (TLA) images. The principle of detection and estimation of along-track attitude jitter is described, and the proposed approach consists of three main components as follows: 1) dense image matching of the TLA images using a comprehensive matching strategy; 2) detection of the back-projection residuals in the line direction caused by attitude jitter; and 3) estimation of the along-track attitude jitter from the back-projection residuals using a genetic algorithm. Experiments were conducted using China's Ziyuan-3 (ZY-3) TLA images, and the experimental results reveal that the frequency of the attitude jitter in the along-track direction ranges between 0.6 and 0.7 Hz, which is consistent with the frequency in the cross-track direction observed in our previous study. In addition, a comparison of the results of the proposed approach with those from direct attitude observations shows good consistency, with as little as 0.1-pixel disparity, which demonstrates the feasibility and reliability of the proposed approach. Furthermore, the geometric accuracy is further improved from a pixel level to a subpixel level and the periodic trend is removed with the compensation of the estimated attitude jitter in addition to the conventional affine compensation, which validates the potential of the proposed approach for geometric accuracy improvement with ZY-3 TLA images.

The grounding line (GL) is the location where ice sheet lose contact with the bed rock. Since this line is under the surface ice, it's hard to define the GL. However, the different basal condition besides the GL caused different vertical movement and a flexure is often formed at the GL. This flexure is detectable by optical images. Meanwhile the ocean-induced vertical movement is detectable by repeat track laser altimetry. In this paper, we use ten scenes of Landsat 8 optical images and NASA's Ice, Cloud, and land Elevation/Geoscience Laser Altimeter System (ICESat/GLAS) data to map the GL of Amery ice shelf (AIS). We develop a least-squares adjustment model to merge the GL derived from these two datasets in order to increase the accuracy of this new line.

The purpose of object matching in conflation is to identify corresponding objects in different data sets that represent the same real-world entity. This article presents an improved linear object matching approach, named the optimization and iterative logistic regression matching (OILRM) method, which combines the optimization model and logistic regression model to obtain a better matching result by detecting incorrect matches and missed matches that are included in the result obtained from the optimization (Opt) method for object matching in conflation. The implementation of the proposed OILRM method was demonstrated in a comprehensive case study of Shanghai, China. The experimental results showed the following. (1) The Opt method can determine most of the optimal one-to-one matching pairs under the condition of minimizing the total distance of all matching pairs without setting empirical thresholds. However, the matching accuracy and recall need to be further improved. (2) The proposed OILRM method can detect incorrect matches and missed matches and resolve the issues of one-to-many and many-to-many matching relationships with a higher matching recall. (3) In the case where the source data sets become more complicated, the matching accuracy and recall based on the proposed OILRM method are much better than those based on the Opt method.