Accurate lunar rock detection is vital for lunar exploration. However, the existing methods are sensitive to factors, such as the uneven lighting and terrain relief. To address these issues, a novel method combining multiscale phase feature type maps (PFTMs) and phase congruency moment maps (PCMMs) is proposed. First, rock seeds are detected through phase congruency and gradient analysis. Second, a strategy called the local scale saliency score (LSSS) is proposed to adaptively estimate the optimal scale layer for candidate rock detection. Within this layer, the specifically designed local-contextual and global-contextual (LGC) features are employed to identify the regions of interests (ROIs) for rocks. Subsequently, the process of filtering false positives (FPs) involves the utilization of geometric metrics and scale feature analysis. Finally, a specially designed edge detector named the bilateral local maximum PCMM-PFTM path is proposed to describe the edges of the rocks. Tests on Chang’E-3 and Chang’E-5 Landing Camera (LCAM) images show the proposed method’s robustness in detecting lunar rocks of varying sizes and reflectance, achieving F1-scores ranging from 0.919 to 0.947.

Multi-modal remote sensing images (MRSI) often suffer from severe nonlinear radiation distortions (NRD) and significant geometric distortions, making precise matching challenging. We developed a feature-based matching algorithm to address this issue using edge consistency scale-space and significant relative response (ECSS). By designing an edge consistency filtering (ECF), we construct a scale space that preserves the structural information of MRSI at various scales, enhancing scale invariance. ECSS computes feature descriptors using multi-orientation filtering techniques to construct significant relative responses. This approach not only resists NRD but also utilizes information from all directional filters to build descriptors with higher discriminative power compared to direct filter responses or the maximum index map (MIM). To ensure rotational invariance, ECSS employs a robust technique for estimating the primary orientation. To further optimize matching accuracy and increase the number of effective matching points, ECSS uses a coarse-to-fine matching strategy. This involves using preliminary matching results to estimate the affine transformation between images, which then guides a more refined secondary matching. We evaluated the performance of ECSS on five different MRSI datasets and compared the results with nine state-of-the-art matching methods: ReDFeat, MINIMA-LG, RIFT, MS-HLMO, SRIF, WSSF, POS-GIFT, OFM, and GLS-MIFT. The experimental results demonstrate that ECSS excels in all performance metrics, particularly in terms of stability and matching accuracy when handling MRSI data with high NRD and complex geometric transformations.

Precision navigation and positioning are essential for lunar landing exploration missions. Terrain-relative navigation based on crater matching provides an effective means for lander position estimation as craters are distinguishing features on lunar. However, challenges arise from the lack of a one-to-one correspondence between image-detected craters and the crater database, as well as the inconsistency of the coordinate system of craters in the image and those in the database, which complicates the matching process. This article has proposed a lunar crater matching method with triangle-based global second-order similarity for precision navigation. First, craters are constructed as triangles as the basic matching primitives, and the topological relationships between craters are transformed into a graph structure. Then, geometric constraints and triangle removal rules are designed to retain high-quality triangles that satisfy the first-order similarity. Next, a second-order similarity metric is introduced to evaluate the consistency of the topology of crater distributions from a global perspective. The global optimal crater matching is determined by constructing a second-order similarity score matrix. The proposed method is validated by comprehensive experiments using both simulation data and Chang’E-6 landing phase data. The experimental results show that the proposed method has achieved the highest accuracy and robustness among the comparison methods, and the average position estimation accuracies are 0.44% and 0.41% of flight altitude for orbiting and landing scenarios.

Digital Surface Models (DSMs) extracted from multi-view satellite images have extensive applications in the filed of photogrammetry. Although Neural Radiance Fields (NeRF) has shown significant potential in 3D reconstruction, most existing NeRF methods for satellite scenes adopt an end-to-end single-branch network structure, making it difficult to achieve fine-grained modeling of multiple complex terrain simultaneously, and performs poorly in weak-texture regions. Meanwhile, deep MLP structures are prone to information degradation during feature transmission, further affecting the completeness and accuracy of DSMs. To address these challenges, we propose RDS-NeRF, a novel NeRF framework integrating residual feature enhancement and depth supervision. The method introduces a residual feature enhancement structure to alleviate the problem of information degradation during feature transmission in the network and improve the model’s ability to model local details and low-texture regions. Additionally, estimated depth maps are incorporated as global geometric priors to guide the network in constructing more accurate and complete 3D structures. Experiments on the WorldView-3 satellite imagery datasets across multiple typical land cover types (building, road, water body, and vegetation) and complex scenes integrating multiple land features demonstrate that RDS-NeRF outperforms mainstream methods in terms of DSM accuracy, completeness, and novel view synthesis quality. Ablation experiments further validate the complementarity and effectiveness of the residual enhancement and depth supervision mechanisms across different scene types. In conclusion, RDS-NeRF provides a new and effective solution for generating high-quality DSMs from satellite imagery with adaptability to multiple scenes. Code will be available at https://github.com/dfsvdgf/RDS-NeRF.

Conventional pixel-level seamline detection algorithms exhibit exponential time complexity on large, batch-mode remote-sensing mosaics, making it difficult to achieve an optimal trade-off between accuracy and efficiency. This paper introduces a globally optimal and highly efficient seamline detection framework. First, a preliminary seamline network is generated by iteratively clipping valid orthoimage regions with a Voronoi diagram, and image blocks are extracted only within overlap areas to markedly reduce data volume. Second, a cost graph constructed on down-sampled blocks is traversed in a reverse-diagonal Z-pattern; a “local entropy–gradient” composite cost function is applied, and a linear-time dynamic-programming (DP) scheme rapidly produces coarse seamlines that bypass texture-rich regions and confine the search space to a narrow band. Third, a buffer centered on the coarse seamline is created, within which an enhanced Dijkstra algorithm performs pixel-level refinement to accurately avoid complex obstacles. Experiments on the GF-7 data set demonstrate that, compared with five representative methods—SMP-DP, A*, Dijkstra, graph-cut, and OrthoVista—the proposed approach improves geometric accuracy by 14.46%, 58.69%, 50.20%, 17.79%, and 69.30%, respectively; processing efficiency is increased by 12.74%, 19.19%, 49.89%, >500%, and 83.72%, respectively. The algorithm has successfully mosaicked 627 GF-7 scenes covering the entire Henan Province, and has yielded similarly favorable results on ZY-3, GF-1 and GF-3 imagery, underscoring its high applicability and robustness for multi-source, large-format remote-sensing production.

This study investigated the effect of the distribution of high-accuracy images and the overlapping degree on combined geometric positioning by further experiments using Geoeye-1 and ZY-3 satellite images. The experimental results revealed that: (a) For combined positioning, reference stereo imagery with different degrees of overlap have different geometric positioning capabilities, especially in the planar direction. Moreover, the overall disparities gradually decrease with the increase of the overlapping degree for the reference stereo images, and (b) the geometric positioning accuracy can be improved by adding more high-accuracy images as references, and the diagonally distributed reference images are more helpful than the single-cornered reference image in improving the combined positioning accuracy. In future work, more and wider range of images would be used for the further validation.

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.

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.

Spaceborne laser altimetry technology assists global DEMs to improve the accuracy of elevation data due to its highly accurate range and wide coverage. As compared to the previous laser altimeter systems used for Earth observation, ICESat-2 has a sensitivity for photon detection that can provide more accurate and denser surface elevation observations. This paper proposed a DEM correction model using ICESat-2 data. The model used the altimetric data to verify the DEM elevation errors in ICESat-2 coverage areas firstly. Then an attribute set was constructed to evaluate the error sources of the global-scale DEM. The evaluations of the error sources include the location/positioning of the platform, atmospheric conditions, topographic relief, land cover, and heterologous infill data, etc. Finally, a regression model was constructed by the attribute set and the DEM elevation errors within ICESat-2 coverage areas, in order to correct the DEM in areas without ICESat-2. In the validation experiments, this study conducted elevation correction experiments using the ASTER Global Digital Elevation Model (GDEM) and the Shuttle Radar Topography Mission (SRTM) in three regions around the world and applied the airborne LiDAR data in each region to verify the corrected results. The results showed that the proposed model was suitable for the elevation correction of global-scale DEMs and can be applied to more than 90% of global land, i.e., land areas with a slope less than 25°. The accuracy improvement ratios of the corrected GDEM were 17.89%–33.33% across the different types of topography, and the accuracy improvement ratios of the corrected SRTM were 27.77%–44.64% across different types of topography.

For time-delay integration (TDI) linear push-broom satellite images, it is generally considered that the matching window size and TDI stage number affect the jitter detection capability, but there are very few quantitative studies. In this letter, a scheme for investigating the effect of the matching window size and TDI stage number on image-based jitter detection is designed, and the model of image motion caused by jitter and the model of jitter detection and estimation for TDI images are deduced and established. Through a comprehensive experimental analysis, it is revealed that the influence of the TDI stage number and matching window size on jitter frequency detection is not significant, which improves our previous understanding. Although the TDI mode will attenuate the jitter amplitude by integration, the real jitter amplitude can be accurately retrieved by the proposed estimation model with attenuation compensation. The matching window size in the row direction has a significant effect on jitter amplitude detection. To ensure the detection accuracy of the jitter amplitude, the matching window size in the row direction is suggested to be less than 1/5 of the image line count corresponding to a jitter cycle.

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.

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.

Ice, Cloud and land Elevation Satellite-2 (ICESat-2), the first photon-counting laser altimetry satellite, is the most advanced on-orbit altimetry system in the world. The data obtained by it contain a large number of background photons, limited by sensitive photon detection system. In this study, a density-based adaptive method (DBAM) for photons detection of ground and canopy method is proposed which is aimed at solving the problem of signal photons detection in vegetation areas. Firstly, the photon density is homogenized according to the noise photon rate, to reduce the effect of uneven background noise. And then the ground signal photons were extracted by the search ellipse adaptively changing direction and size along the slope direction to find the maximum density direction. The canopy signal photons were extracted again from the rest photons of the first step by using of a vertical elliptical search area. At last of the DBAM, the photons between the ground and the canopy are extracted as vegetation signal photons. The effectiveness of DBAM is evaluated both quantitatively and qualitatively. The results show that proposed method can effectively detect ground and canopy photons, with Kappa coefficient of 0.88 and 0.91 of two selected datasets, respectively. Compared to the results of ATL08, DBAM can better adapt to the slope, the extracted ground photons have better continuity, and can extract more accuracy vegetation photons.

Due to its high elevation accuracy and wide coverage, satellite laser altimetry plays an important role in many scientific fields, such as polar ice sheet monitoring, vegetation canopy height measurement, and topography mapping. However, the elevation accuracy of satellite laser altimetry data is affected by many factors, such as the atmosphere, instrument noise, terrain fluctuation, etc., which leads to an uncertain accuracy. In this letter, to solve this problem, we propose a method based on random forest to extract satellite laser altimetry footprints that meet the elevation accuracy requirements of certain applications in complex terrain. Using ICESat, we take the elevation control point accuracy requirement for 1:10 000 mapping as an example to verify the proposed method. Experimental results show that the elevation root mean square errors (RMSEs) of the selected high-quality footprints are 0.41, 0.70, and 0.87 m in flat land, hills land, and mountainous areas, respectively, which meets the requirements of 1:10 000 topography mapping. The percentage of extracted footprints that meet the elevation accuracy requirement from the three terrains are all higher than 90%.

Satellite stereo mapping, together with laser altimetry, can be used to obtain three-dimensional geospatial information. Spaceborne laser altimeter can provide high-accuracy elevation information; however, due to the lack of detailed intensity information, its planimetric accuracy is usually worse than the ranging accuracy. The Chinese Gaofen-7 (GF-7) satellite, which was designed for civilian mapping application, was launched on November 3, 2019. The GF-7 satellite’s main payloads are a laser altimeter system (with footprint camera) and a dual-linear charge-coupled device (CCD) mapping camera. According to the pixel coordinate of the laser footprint in the stereo image, the laser altimeter together with the footprint camera can provide planimetric geodetic coordinates for the control points of a higher accuracy than the other traditional satellite laser altimeters, and represents a new technology for satellite mapping. In this article, a laser footprint planimetric location method for the GF-7 satellite is proposed. The method is designed based on the main payload characteristics of GF-7 and the working modes of the laser altimeter by the combined use of subpixel phase correlation image matching and four types of laser spot center detection methods. The planimetric positioning accuracies of the laser spots in urban, suburban, farmland, forest, mountainous, and ice sheet areas were also analyzed. The experimental results show that the accuracy of planimetric location relative to stereo image for the laser footprint is 0.3–1.0 m (except for ice sheets ~12 m) when the footprint camera works under the synchronous mode, and 0.2–0.4 m when the footprint camera works under asynchronous mode (AM).

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.

Chang'E-4's landing on the far side of the moon is the first time in the world. The overall slope in Von Kármán crater is relatively gentle, while some areas are relatively steep. There are various impact craters and boulders in the region, and the illumination conditions are different. Therefore, topography and illumination conditions need to be investigate to ensure the safety of the lander and rover. In our work, the slope analysis, crater extraction and illumination analysis of the Von Kármán area are carried out using 30 m resolution DEM generated from LOLA data. Then for the specific landing site, high resolution DEM and DOM are generated from LROC NAC images, and the impact craters, boulders and other topographic features in the landing area as well as a precise slope map are then extracted. Which provides high-precise spatial information to support the scientific exploration of Chang'E-4 project.

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.

We propose a new framework of systematic fracture mapping and major calving event prediction for Filchner-Ronne Ice Shelf (FRIS), Antarctica using multisource satellite data, including optical imagery, SAR imagery, altimetric data, and stereo mapping imagery. 2D mapping of fractures in FRIS is conducted using optical and SAR images and 3D information of two large rifts, Rift 1 and 2, are extracted from ZY-3 and WorldView-2 stereo images as well as ICESat data. Based on the results of the 2D and 3D fracture mapping, the spatial and temporal pattern of the overall fracture changes and large rift evolution are estimated and analyzed. The overall fracture observations do not seem to suggest immediate significant impacts on the stability of the shelf. However, the most active regional fracturing activities occurred at the front of Filchner Ice Shelf. The fracture mapping results can be used to improve the reliability of ice shelf modeling and as support for further analyses of ice shelf stability.

A long term assessment of the Antarctic ice sheet elevation change trend from 2003 to 2016 has been carried out using a combination of ICESat (2003-2008), Envisat (2003-2008) and CryoSat-2 (2010-2016). The contemporaneous ICESat and Envisat results showed a consistency in the estimated elevation change rate of Antarctica during 2003 to 2008: 0.1±0.1 cm a-1to 0.3±0.6 cm a-1, respectively, which were then combined as the overall elevation change of Antarctica during 2003 to 2008. The recent CryoSat-2 result suggested an overall elevation change rate of Antarctic ice sheet of from 2010 to 2016. Furthermore, the elevation change of East Antarctica is relatively small, while West Antarctica appears to have an elevation decrease trend. The above is based on our preliminary data processing and analysis results. The high uncertainties in Basin 15 and Antarctic Peninsula need to be further investigated. We will report our improved results at the conference.

Ice flow velocity is used to estimate ice mass changes in glaciers and is a significant indicator of the stability of the Antarctica ice sheet in global change studies. The existing regional Antarctica ice flow speed maps are usually derived from radar or optical satellite observations of modern satellites since the 1970s. This paper presents a new analytical photogrammetric method for estimating Antarctica ice flow velocity fields by using film-based stereo ARGON photographs collected in the 1960s. The key of the proposed innovative method is a parallax decomposition that separates the effect of the terrain relief from the ice flow motion. An innovative implementation strategy is developed by using a framework that involves key techniques of hierarchical stereo image matching, ice flow direction determination, parallax decomposition, and ice flow speed estimation. This method is applied in the Rayner glacier in eastern Antarctica by using two sets of ARGON images with a two-month interval in 1963. The produced digital terrain model and speed map achieved a ground position accuracy of 61 m and a speed accuracy of 70 m a-1. A comparison with recent products from 2000 to 2010 shows no significant topographic changes in the study area. Furthermore, the speed around the grounding line remained at the same level, while the speed in the ice shelf front decreased by 73 m a-1. The ice shelf front advanced by approximately 7 km over more than 40 years. Overall, the observation results indicate favorable conditions for the stability of the Rayner glacier-ice shelf system.

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.

In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

Platform oscillation is a crucial error source that undermines the geometric performance of satellite imagery. In this paper, an approach for oscillation detection that utilizes the parallax observation between multispectral bands is proposed. Due to the parallax observation configuration of the multispectral sensors, the attitude oscillation of the ZY-3 satellite can be detected and estimated by the parallax disparities between adjacent band images. The parallax disparities between bands are obtained through a high-accuracy image matching method based on phase correlation at the subpixel level. The pixel displacements caused by the satellite oscillation are then retrieved from the parallax disparities by the use of two proposed transformation models. Experiments using both single-scene and long-strip images were conducted in order to retrieve the frequencies and amplitudes of the oscillation, as well as its changing trend. The experimental results for the ZY-3 satellite demonstrate the following findings: 1) the oscillation components obtained contain a distinct frequency of around 0.65 Hz; 2) the amplitude of the oscillation displacement on the image plane ranges from 0.5 to 1.5 pixels in the cross-track direction and from 0.2 to 0.6 pixels in the along-track direction, respectively; and 3) the oscillation frequency detected from the images is in agreement with that from the original attitude data.

Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.