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.

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.

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.

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.

On May 15, 2021, China’s first Mars rover, the Zhurong rover successfully landed on the Utopian Planitia in the northern region of Mars. The multispectral camera (MSCam) on board the rover has captured multi-spectral images, which provide spatial and spectral information about in-situ observation targets and facilitate analysis of the types of materials on the Martian surface. However, frequent sandstorms on Mars are accompanied by dust deposition, and varying degrees of dust coverage have altered the original spectral characteristics of scientific detection targets, resulting in inaccurate material inversion. To address this issue, a novel in-situ dust cover index (IDCI) is proposed. The data-driven method is based on the spectral features of dust cover in the MSCam multispectral bands. It provides a wealth of information through a simple yet effective calculation that maximizes the discrimination between different categories of dust-impacted areas and estimates the degree of dust coverage. Experimental results obtained from 17 scientific observations by MSCam along the Zhurong rover’s routing path confirm the effectiveness of the proposed IDCI. It effectively distinguished dust-free areas from invalid areas (e.g., shadows), while reflecting the degree of dust coverage in the scene. The IDCI demonstrated its superior performance, operating up to three times faster than other reference methods. Additionally, it exhibited a notable advantage over other techniques, achieving a variance ration criterion (VRC) for target separation that was at least 5% higher. These results highlight the efficiency and effectiveness of the proposed IDCI, establishing it as a valuable tool for Martian surface analysis.

Subglacial lakes are an important part of the Antarctic basal hydrological system, with many active subglacial lakes distributed in the steep topography of eastern Antarctica. When the ice surface has a slope, the horizontal geolocation error and elevation error of ICESat-2 altimetry data introduce additional uncertainty in estimating the ice surface elevation change, which will affect our ability to precisely monitor the activities of subglacial lakes. Therefore, based on the classical repeat-track analysis, we use weighted total least-squares adjustment to quantify the impact of horizontal geolocation and elevation errors on the fitted ice surface elevation. Through an iterative process, we derive precise time series of elevation changes to meet the need for monitoring subglacial lake hydrological activities. Using this improved method, we obtained the volume changes of the CookE2 subglacial lake in East Antarctica from March 2019 to March 2023. The results showed that Lake CookE2 was continuously recharged by subglacial water during this period, with an average equivalent recharge rate of 0.054 km³/yr. The water supply in the main lake accounted for about 68.5% of the total. The SW lobe, located away from the main basin, exhibited hydrological activity again after the drainage event and was reclassified into the active lake domain. The new lake area is ~ 118.73 km2. Compared to the traditional method, the improved approach reduces the variance of unit weight by ~0.044 m in the lake basin and by ~0.062 m on the steep southern basin margin. The precision of elevation change in rugged topography or steep slope areas has been significantly improved, aiding in precisely monitoring of subglacial lakes water volume changes and in determining their outlines.

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.

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.

Understanding basal processes across ice-sheet grounding lines is crucial in accurately modeling ice-sheet dynamics and estimating global sea-level rise. The grounding line, which demarcates the specific boundary between a grounded ice sheet and a floating ice shelf, is notoriously challenging to locate precisely. Existing methods for determining grounding line location rely on indirect methods, such as tide-induced vertical ice-shelf motion (the point of flexure determining the grounding line) and ice-surface slope change (the sharp change in gradient toward being flat indicating the grounding line). In this study, we utilize ice-penetrating radar data to extract grounding line information from the Lambert-Amery glacier system. By incorporating ice bed topography and the reflection amplitude differences between ice-water and ice-bedrock interfaces, we establish an automated method to extract grounding line positions from radar survey lines. From 53 radar survey lines, we identified 85 grounding points. The comparison with the positions from an existing satellite InSAR-based grounding line product shows an average difference of 0.69±0.70 km. Tidally-induced migration of grounding lines at different points of the tidal cycle, and advance/retreat of grounding line with the evolution of the ice shelf, are the main reasons for the discrepancy between the radar-derived results and the existing grounding line products. In general, the results demonstrate the feasibility of ice-penetrating radar in confirming grounding line positions, and show great potential in constraining indirect satellite remote sensing or modelling evaluations at both regional and continental scales. Our work facilitates an ongoing effort of the Scientific Committee on Antarctic Research (SCAR)’s RINGS programme to develop gapless coverage of bed topography in the coastal regions around Antarctica.

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.

The Martian surface landforms are highly related to the safe landing and traversability of Mars rovers. Furthermore, landforms associated with the presence of water/ice, minerals and biosignatures can provide valuable insights for Mars exploration missions, particularly in relation to the selection of landing or sample collection sites. The small number of Martian landform datasets and the scarcity of labelable landform samples over Mars make the precise mapping of Martian landforms a challenging task. In this article, we propose a stepwise deep feature transfer (SDFT) model for the mapping of Martian landforms with a small number of labeled samples. The SDFT model comprises two transfer steps. In the first transfer step, a deep learning model trained on a large public source dataset from Earth is transferred to a medium sized public dataset from Mars. This transfer is conducted through a standard pre-training and fine-tuning procedure utilizing a linear classifier. In the second transfer step, the model is further transferred to a small number of target datasets on Mars through a pre-training and fine-tuning procedure with a cosine distance classifier. The stepwise training technique mitigates the challenges associated with varying datasets and small training samples. The proposed SDFT model has been validated on two self-built sample sets using images from the Mars Reconnaissance Orbiter’s Context Camera (CTX). It has also been employed for landform mapping in two local regions with small samples to evaluate its effectiveness in comparison with existing state-of-the-art methods.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimetry System (ATLAS), enhances ice surface elevation change estimations. ATLAS significantly improves elevation accuracy from over 10 cm, as seen in ICESat’s full-waveform technique, to 2-4 cm. ICESat-2 data’s elevation quality is currently validated at the single-pulse level using the corner cube retro-reflector (CCR) method, a terrain-independent technique insensitive to ice surface conditions. This study introduces a cost-effective CCR system for validating ICESat-2 elevation data from Antarctica’s firn surface, complementing the ICESat-2 Science Team’s approach. Through three validation campaigns in Antarctica and Shanghai, China, we enhanced our CCR system in design of the prism hardware, alignment of the CCR array, and determination of valid photon data. We implemented a self-adaptive window determination strategy for valid CCR-returned signal photons to minimize the impact of inadequately returned photons from the Fraunhofer diffraction pattern’s lobes and two closely elevated CCRs. The accurate elevations of single pulses from each CCR were determined and analyzed using Global Navigation Satellite Systems (GNSS) in-situ observations during the ICESat-2 overpasses in 2020–2022. Our refined CCR validation system shows that ICESat-2’s pulse elevation has a bias of less than 3.0 cm and a precision of ±1.7 cm in the latest experiment on the Nansen ice shelf, East Antarctica.

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.

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.

Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) carries a photon-counting laser altimeter with an unprecedented elevation accuracy of 2–4 cm. Since its data availability in 2018, there has been a challenge for the establishment of a new data processing model that can take advantage of this satellite for accurately estimating volumetric changes in Antarctica and associated contribution to global sea level rise (GSLR). We introduce an innovative multitemporal elevation change estimation model (MECEM) that separates precipitation effects from topographic influences to eliminate their correlations and estimates the elevation change rates effectively through a spatiotemporal iterative procedure. The MECEM results are validated by using GNSS in situ observations, snow stakes measurements, and airborne altimetric survey data. The results are also compared with those from ICESat and ESA multimission radar altimetric dataset. It is demonstrated that the model is capable of estimating small thickening of$1.8~\pm ~0.1$cm yr1 in the Vostok subglacial lake region. Using ICESat-2 ATL06 data from 2019 to 2023, the model is proven to be effective in the estimation of elevation change rates in Antarctic basins of different characteristics. Our results show that an increase of$0.103~\pm ~0.001$m yr1 in thickening is found from 2017–2021 to 2019–2023 in Dronning Maud Land. Furthermore, an accelerated thinning by$- 0.12~\pm ~0.035$m yr1 is witnessed from 2003–2019 to 2019–2023 in the fast-flowing Pine Island Glacial. With more ICESat-2 data acquired, the developed MECEM model can be applied for estimating the contribution of the entire Antarctic ice sheet (AIS) to GSLR.

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.

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.

In situ detection on Mars can provide detailed information on the planet’s topography and material composition while also validating the results obtained by orbiter probes. The laser-induced breakdown spectroscopy (LIBS) has emerged as a popular technology for Mars in situ exploration due to its fast response and high accuracy in identifying elements. The analysis of LIBS data obtained by different in situ scientific payloads onboard Mars rovers can help explain scientific problems related to about Martian geological genesis and history. However, it is essential to correct the data acquired by different instruments for joint analysis and to facilitate scientific discoveries due to variances in instrument specifications and data acquisition conditions. This article presents a novel cross-instrument spectral harmonization (CISH) approach that can eliminate differences in intensity and peak positions in LIBS spectra from different instruments. In particular, a peak position consistency correction (P2C2) method is proposed to correct cross-instrument peak position inconsistency by eliminating noise or irregular bumps presented in the LIBS spectra that may be incorrectly identified as characteristic peaks. The proposed CISH approach was validated using real Mars in situ LIBS data acquired by the chemistry and camera tool (ChemCam) and Mars surface composition detector (MarSCoDe). The experimental results demonstrate increased consistency in intensity and peak positions. Specifically, the average intensity difference decreased from 3.1287 to 2.1898, and the average peak position difference decreased from 0.1540 to 0.0335 nm. Meanwhile, the accuracy of inversion after consistency correction for the same calibration target (Norite) was also improved. The average root-mean-square error (RMSE) of eight oxides decreased from 5.18 to 3.37 by using a support vector machine (SVM) and from 18.80 to 7.07 using a partial least squares-submodel (PLS-SM). The proposed approach has the potential to establish a uniform benchmark for LIBS data acquired by different instruments at different times and locations, ensuring data consistency and comparability of identified material composition results.

Hyperspectral images (HSIs) is consisted of many narrow spectral bands which are capable of recording abundant features including both the spectral and spatial signatures information which have been widely used in various fields, such as urban planning, disaster monitoring. Due to the large number and high similarity of the collected spectral brands, many methods are developed to handle the problem of extracting effective features. Recently, many CNN-based methods have been proposed by exploiting the spectral-spatial signatures of the HSIs data and achieved promising results. Although many methods adopt patch-based input pattern to emphasize the importance of the spatial neighbor information of each pixel, the relations are still limited to a small area around the pixel and the latent relations among the pixels belonging to different semantic categories at the boundary are still not well exploited. To explore the relationships between pixels from a more global perspective, a neighbor-based relation mining framework is proposed to explore the long-range relations among different local regions. Experiments are conducted on two hyperspectral image classification datasets and the results demonstrate the effectiveness of the proposed long-range relations mining scheme by comparison with some state-of-the-art methods.

Scene classification is one of the most commonly studied areas of parsing the earth observation data. How to effectively interpreting the remote sensing images and extracting informative features are the great challenges for remote sensing image classification. Many important applications, such as land management and urban analysis, are based on the performance of remote sensing classification model. Recently, a lot of CNN based methods have been proposed and achieve promising results. Inspired by the success of knowledge distillation which transfers the learned information from a teacher model to a student model, a knowledge distillation based framework is proposed in this paper to handle the task of remote sensing scene classification from coarse to fine. Specifically, the learned knowledge from the teacher network is transformed into the coarse soft label and fine output mask to better guiding the student network to learn more informative features. Experiments are conducted on two widely used remote sensing scene datasets to evaluate the effectiveness of the proposed method and achieve comparable results compared with some state-of-the-art methods.

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.

Aerial image Segmentation segmentation faces intrinsic foreground-background imbalance and background clutter distraction. To guide the segmentation model to learn more discriminative foreground ability and more invariant back-ground representation features, we design a Deformable Point Network (DPNet). It is an end-to-end segmentation network and consists of a multi-head deformable attention module that simultaneously considers foreground object information and background suppression. Specifically, we first employ a feature pyramid network to aggregate multiple-layer features to handle scale variants. And then, we further investigate deformable convolution to select some representative points for each layer and propose a differential module to implement it automatically instead of traditional dense fusion. Moreover, we incorporate the multi-head mechanism in the feature fusion to focus on the key contents from different representation regions. Experimental results on the representative iSAID, Vaihingen, and Postdam datasets demonstrate that our DPNet achieves competitive performance. Also, the multiple-head deformable attention facilitates the network convergence significantly.

Accurate detection of the multiple classes in aerial images has become possible with the use of anchor-based object detectors. However, anchor-based object detectors place a large number of preset anchors on images and regress the target bounding box while anchor-free object detections predict the location of objects directly and avoid the carefully predefined anchor box parameters. Object detection in aerial images is faced with two main challenges: 1) the scale diversity of the geospatial objects; and 2) the cluttered background in complex scenes. In this letter, to address these challenges, we present a novel Anchor-Free Network with a Density map and attention mechanism (DA2FNet). Considering the extreme density variations of the detection instances among the different categories in aerial images, the proposed DA2FNet model conducts density map estimation with image-level supervision for the geospatial object counting, to acquire global knowledge about the scale information. A simple and effective image-level global counting loss function is also introduced. In addition, a compositional attention network is further introduced to enhance the saliency of the foreground objects. The proposed DA2FNet method was compared with the state-of-the-art object detection models, achieving excellent performance on the NWPU VHR-10, RSOD, and DOTA datasets.

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.

Machine learning methods for water depth estimation using remote sensing require accurate prior depth measurements. The successful operation of the ICESat-2 mission provides depth in shallow water directly; however, its spatial coverage is limited. Machine learning has been used to link optical remote sensing images and ICESat-2 data for bathymetric mapping. Compared with other machine learning models, convolutional neural network (CNN) models utilize the local spatial correlation between adjacent pixels and can thus reduce the effect of environmental noise. However, existing CNN and other machine learning models rely on data mining to build a general relationship between water depth and spectral information, and they ignore the known physical law. In this paper, we propose a physics-assisted convolutional neural network (PACNN) model. This model incorporates knowledge from radiative transfer theory into a normal CNN model by building a series of spectral feature input layers. In the PACNN model, the spectral information, which is directly related to water depth, is emphasized. Multitemporal ICESat-2 data and Sentinel-2 images were used to validate the model. In experiments with data from three study areas, the PACNN model outperformed the existing CNN model, achieving an accuracy of over 98%. The proposed method can effectively solve the underestimation in deeper water (20–30 m) and reduce the variance of estimates. The superiority of the PACNN model demonstrates how a machine learning model can be assisted by physics theory.

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.

Among various multimodal remote sensing data, the pairing of multispectral (MS) and panchromatic (PAN) images is widely used in remote sensing applications. This article proposes a novel global collaborative fusion network (GCFnet) for joint classification of MS and PAN images. In particular, a global patch-free classification scheme based on an encoder-decoder deep learning (DL) network is developed to exploit context dependencies in the image. The proposed GCFnet is designed based on a novel collaborative fusion architecture, which mainly contains three parts: 1) two shallow-to-deep feature fusion branches related to individual MS and PAN images; 2) a multiscale cross-modal feature fusion branch of the two images, where an adaptive loss weighted fusion strategy is designed to calculate the total loss of two individual and the cross-modal branches; 3) a probability weighted decision fusion strategy for the fusion of the classification results of three branches to further improve the classification performance. Experimental results obtained on three real datasets covering complex urban scenarios confirm the effectiveness of the proposed GCFnet in terms of higher accuracy and robustness compared to existing methods. By utilizing both sampled and non-sampled position data in the feature extraction process, the proposed GCFnet can achieve excellent performance even in a small sample-size case. The codes will be available from the website: https://github.com/SicongLiuRS/GCFnet.

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).

Geospatial object detection has made considerable progress with the use of anchor-based object detectors. In such a situation, the detection performance relies heavily on the parameter settings of anchor boxes. We present a Single Shot Anchor-Free Network (SSAFNet) to tackle with this problem. By eliminating the anchor boxes, the SSAFNet completely avoids the carefully predefined anchor boxes parameters and the computation for adapting the huge scale variation of geospatial objects. A compositional attention network is further introduced to enhance the saliency of foreground objects. We evaluate the SSAFNet on the representative NWPU VHR-10 and RSOD datasets, achieving competitive performance with state-of-the-art anchor-based detection methods.

The spatio-temporal pattern of cultivated land and its changes are of great significance in the study of ecology, geography and agronomy. In this study, long time series remote sensing information is used to monitor the spatio-temporal pattern and phenological characteristics of cultivated land on Chongming Eco-island, Shanghai, China. Based on MODIS-NDVI products (2012-2018), NDVI expectations curves of Chongming Eco-Island are established. It is demonstrated that the negative peaks of NDVI expectation curve in June become less noticeable after 2016. After that, NDVI image difference is performed between the time points at the positive peaks and negative peaks. The results indicate that the reason for the NDVI pattern changes these years may be related to the policy that wheat reduction and green manure enhancement implemented in Chongming Eco-island. Therefore, crops are diversified, and the phenological period of crops on the island is no longer sheer two cropping per year. This study is helpful to the ecological structure adjustment of Chongming Eco-island and the scientific management of cultivated land.

The space-borne earth observation LiDAR satellite Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) equipped with photon counting LiDAR system was launched on September 15, 2018. Significantly different from waveform laser altimeter carried on ICESat, the multiple beams photon counting laser altimeter can better measure Earth' surface and directly provide the location of the scattering event. Due to the high sensitivity, the photon detector responds photons come from the solar background and the atmospheric scattering, which causes plenty of noise. It is necessary to study an effective method to identify signal photons from noise. This paper aims to study serval typical noise filtering methods and discuss their characteristics. The experiments are conducted on both ice sheet and vegetation data sets. The results are presented and some suggestions for choosing which denoising method to face in different environmental conditions are given.

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.

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.

Hyperspectral remote sensing image (HSI) clustering can be defined as the process of segmenting pixels into different sets that satisfy the requirement that the differences between sets are much greater than the differences within sets. According to the fast density peak-based clustering algorithm, we propose an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels. For the metric of the density, we present an adaptive-bandwidth probability density function using pixel numbers as the input and the calculated pixel local density as the output, which determines the bandwidth on the basis of the Gaussian assumption. For the metric of the distance, in order to obtain a pixel-level spectral distance, we calculate the Euclidean distance between pixel vectors from the multiple bands. In the proposed approach: 1) use the least-squares method for the curve fitting of the two results; 2) eliminate outliers based on the Pauta criterion; 3) adopt regression calculation; and 4) obtain the cluster centers according to the classification criteria of the local density and the distance between pixel vectors. The other noncluster center points are clustered based on their similarities with the cluster centers by iteration. Finally, we compare the results with those of other unsupervised clustering methods and the reference data sets.

In this paper, a new joint spectral-spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence.

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.

There are a variety of remote sensing products of Antarctica that cover different time periods and published by different research groups. The resolution and accuracy for these products are quite different due to original data sources, extraction methods, etc. The aim of this research is to assess the quality and investigate changes that can be detected in three main categories of Antarctic remote sensing products including groundling lines, coastlines and surface elevation models (DEM). All the products are available at National Snow and Ice Data Center (NSIDC). In order to identify changes and uncertainties of these products, cross-validation strategies are developed for the quality assessment when there is a lack of ground truth data. The quality assessment results for each remote sensing product are presented in the paper.

A hierarchical method for subpixel surface water mapping accounting for the high spectral heterogeneity of urban materials is proposed in this paper. Specifically, we first applied water index (WI) for remote sensing image classification at pixel level, afterwards, the land, water, and land-water mixture can be extracted automatically. Then the spectral mixture analysis (SMA) is applied to land-water mixed pixels for water fraction estimation at subpixel level. To obtaining the most representative endmembers in SMA, we designed an adaptive iterative endmember selection method based on the spatial similarity of adjacent pixels. The proposed hierarchical processing method based on WI and SMA (WISMA) is applied to urban areas for reliability evaluation using the Landsat-8 Operational Land Imager (OLI) images. For comparison, four methods at pixel level and subpixel level were chosen respectively. Results indicate that the water maps generated by WISMA correspond as closely with the truth water maps with subpixel precision. And the results showed that the WISMA achieved the best performance in water mapping with comprehensive analysis of different accuracy evaluation indexes (RMSE and SE).

Hyperspectral image super resolution (SR) reconstruction has been studied widely and many algorithms have been proposed. In this paper, a novel super resolution reconstruction method was designed by employing a joint spectral-spatial sub-pixel mapping model which aims to obtain the probabilities of sub-pixels to belong to different land cover classes by dividing mixed pixels into several sub-pixels. Given these sub-pixel probabilities, the resolution enhanced image can be further generated. The proposed approach has been evaluated using both synthetic and real hyperspectral images and compared with other well-known methods. The visual and quantitative comparisons confirm the effectiveness of the proposed method.

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.

This paper presents a new subpixel mapping method based on subpixel attraction-repulsion. The proposed method is formulated as an optimization problem with respect to attraction-repulsion among subpixels and is used to reconstruct a finer spatial resolution image from a lower resolution one. A comprehensive experiment is conducted to demonstrate the performance of the proposed method, by comparing it with the other three existing subpixel mapping methods, i.e., linear optimization, pixel swapping and spatial attraction model methods. In the experiment, both a synthetic image with known fractional abundances and an EO-1 Hyperion hyperspectral image of Shanghai were used to evaluate performances of the subpixel mapping methods. The experimental result shows that by using spatial dependence with attraction between the same types of ground objects and repulsion between different types of these objects, the proposed subpixel mapping method achieves a better performance on subpixel mapping than the other three methods.

A new approach for monitoring water quality is proposed based on quantitative remote sensing in Huangpu River, Shanghai. The data processing for multi-spectral remote sensing imagery is first presented. The inversion models for two typical water quality parameters - Dissolved oxygen (DO) and Secchi disk (SD) are then developed. Based on the derived models and multi-temporal remote sensing imagery, the spatial- temporal analysis for the water quality variation is therefore conducted. The results show that the proposed models can detect effectively the temporal and spatial distribution of water quality.