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

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.

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.

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.

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.

Temporally neighboring homologous images are crucial to provide auxiliary information for thick cloud removal. Due to the inherent satellite revisit period and frequent cloud obscuration, there is often a significant time interval between the target cloudy images and neighboring cloud-free homologous images, leading to potential land surface condition changes. Moreover, multitemporal cloudy images that may contain valuable complementary information in the noncloudy regions, are often neglected in practice. This article focused on thick cloud removal from Landsat 8 OLI images. We proposed to fuse the temporally more frequent Sentinel-2 MSI images and also cloudy multitemporal images consisting of Sentinel-2 MSI and Landsat 8 OLI time-series. Acquired by a sensor different from Landsat 8 OLI, Sentinel-2 MSI images exhibit great similarities in data characteristics. To fully exploit the spatio-temporal-spectral information in multisource and multitemporal auxiliary images, we proposed a novel deep network called MST-Net. MST-Net was validated using 12 simulated and two real cloudy Landsat 8 OLI images. The results show that the MST-Net can produce more satisfactory predictions than the five benchmark methods. Both the images acquired by a different sensor and homogeneous multitemporal cloudy images are beneficial. Under different sizes of clouds, the MST-Net produces consistently the most accurate predictions. Furthermore, due to the fusion of all bands simultaneously in the temporally closest Sentinel-2 MSI images, the MST-Net is less affected by thin cloud occlusion errors. Overall, the MST-Net shows great potential for cloud removal from optical images produced by a wide range of sensors and, more generally, filling gaps in various global scale products.

Accurate estimation of canopy height is crucial for monitoring forest health, carbon cycling, biodiversity, and climate change. Existing canopy height mapping methods often integrate Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with optical or radar remote sensing images. However, these methods typically establish relationships between single GEDI relative height (RH) metrics (e.g., RH95 or RH98) and surface reflectance or backscatter signals, overlooking valuable information from multiple RH metrics. In this study, we developed a multiple relative height metrics-based canopy height mapping (MRH-CHM) model, which was implemented using deep learning based on the Google Earth Engine (GEE) cloud platform. The MRH-CHM model integrates features from multiple GEDI RH metrics (e.g., RH0 to RH100 with an interval of 10 units) and also Landsat-8 and Sentinel-1 images. To deal with the distinct features in the multimodal data, in the proposed MRH-CHM method, the convolutional neural network (CNN) and multi-layer perceptron (MLP) modules were designed separately before a feature fusion process. Using the MRH-CHM model, a canopy height map of China for the year 2020 was produced at 30 m spatial resolution. The MRH-CHM results present greater accuracy than Lang’s, Potapov’s, and Liu’s products, achieving the lowest Root Mean Square Error (RMSE) of 5.74 m and the largest correlation coefficient (r) of 0.78 when validated against 6,168,244 hold-out GEDI validation data points. The produced map provides valuable scientific data for policymakers, researchers, and forest stakeholders to monitor forest health and biodiversity and to guide efforts toward carbon neutrality. The produced canopy height map is publicly available at https://doi.org/10.5281/zenodo.11560195.

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.

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.

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

The 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.

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.

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

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

In this letter, wavelet synchrosqueezing transformation (WSST)-based technique is introduced for ground penetrating radar (GPR) near-surface void recognition. WSST can extract the signal features effectively because the ground surface reflection or antenna coupling suppresses the target echo. However, these features are distorted by mathematical procedures and clutter. A shallow neural network (SNN) is applied to estimate the possible targets in this case. The SNN only has full connection layers, and its training sets are simulated data under different levels of SNR. After the network is trained, the result scalar can be used to identify the near-surface targets. Simulation and experimental results show that the proposed method achieves 1/10 range resolution binary classification. Compared with recent studies, the detectable thickness improves (at least two times smaller). Besides, the method has a limited-size network and can be trained in situ, which makes it more suitable for engineering uses.

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.

Landsat surface temperature (LST) is an important physical quantity for global climate change monitoring. Over the past decades, several LST products have been produced by satellite thermal infrared (TIR) bands or land surface models (LSMs). Recent research has increased the spatio-temporal resolution of LST products to 2 km, hourly based on Geostationary Operational Environmental Satellites (GOES)-R Advanced Baseline Imager (ABI) LST data. The spatial resolution of 2 km, however, is insufficient for monitoring at the regional scale. This paper investigates the feasibility of applying spatio-temporal fusion to generate reliable 100 m, hourly LST data based on fusion of the newly released 2 km, hourly GOES-16 ABI LST and 100 m Landsat LST data. The most accurate fusion method was identified through a comparison between several popular methods. Furthermore, a comprehensive comparison was performed between fusion (with Landsat LST) involving satellite-derived LST (i.e., GOES) and model-derived LSMs (i.e., European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysisv.5 (ERA5)-Land). The spatial and temporal adaptive reflectance fusion model (STARFM) method was demonstrated to be an appropriate method to generate 100 m, hourly data, which produced an average root mean square error (RMSE) of 2.640 K, mean absolute error (MAE) of 2.159 K and average coefficient of determination (R2) of 0.982 referring to thein situtime-series. Furthermore, inheriting the advantages of direct observation, and the fusion of Landsat and GOES for the generation of 100 m, hourly LST produced greater accuracy compared to the fusion of Landsat and ERA5-Land LST in the experiments. The generated 100 m, hourly LST can provide important diurnal data with fine spatial resolution for various monitoring applications.

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.

Landform classification and mapping of the Martian surface using Mars orbiter images can provide an important reference for landing site selection and rovers’ traversability evaluation in Mars exploration. Moreover, specific Martian landforms are closely associated with the evidences of water-related activities and Martian life, thus have crucial research importance. This article proposes a novel superpixel-guided multiview feature fusion network (MarsMapNet) for efficient mapping of the Martian landforms. In particular, the proposed MarsMapNet first generates the superpixel-level segments from Mars orbiter images by considering local morphological homogeneity of landforms. Then, a multiview feature extraction and fusion (MVF) network is developed, where abstract convolutional features are extracted based on scene-level patches, and multitextures are extracted based on local landform from shallow-to-deep feature learning. After the network being trained on scene-level samples and guided by the superpixel segmentation, Martian landforms can be correctly classified in an efficient way, whose mapping time cost sharply decreased when compared to the reference methods. The proposed MarsMapNet has been validated on three real landing sites from several Mars missions (i.e., the Jezero Crater, the Southern Utopia Planitia, and the Oxia Planum) by using the Mars Reconnaissance Orbiter’s Context Camera (CTX) images. Qualitative and quantitative analyses on the obtained experimental results confirm the effectiveness and efficiency of the proposed MarsMapNet when compared with the state-of-the-art (SOTA) methods, demonstrating its potential for supporting a Martian global landform mapping in the future.

For urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.

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.

Highway crack segmentation is a critical task for highway infrastructure monitoring and maintenance. While imagery from unmanned aerial vehicles (UAVs) is applied to the task of highway crack segmentation, it has great prospects in terms of speed and range. However, it is difficult to accurately identify road cracks from UAV remote sensing images, because the cracks are very narrow and small, often containing only a few pixels. To improve the segmentation of road cracks in UAV images, this study proposed an improved identification technique based on the U-Net architecture enhanced with a convolutional block attention module, an improved encoder, and the strategy of fusing long and short skip connections. A public road crack dataset was relabelled for network training and a UAV remote sensing road crack dataset containing 1157 images was used to verify the generalization ability of the enhanced network model. Results showed that the proposed method could effectively predict highway cracks in UAV images, with mean intersection over union (mIoU) of 77.47% and crack accuracy of 68.38%, which was better than the traditional U-Net model and some traditional semantic segmentation models. The proposed network is trained quickly by public dataset and can predict the road cracks on the new UAV images with high crack accuracy. This study provides an effective solution for the need to quickly grasp the damage status of roads over a wide area in the case of earthquake and other natural disasters. The highway crack segmentation benchmark dataset has been open sourced at:https://github.com/zhhongsh/UAV-Benchmark-Dataset-for-Highway-Crack-Segmentation.

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.

With the increasing availability and resolution of satellite sensor data, multispectral (MS) and panchromatic (PAN) images are the most popular data that are used in remote sensing among applications. This article proposes a novel cross-resolution hidden layer feature fusion (CRHFF) approach for joint classification of multiresolution MS and PAN images. In particular, shallow spectral and spatial features at a global scale are first extracted from an MS image. Then, deep cross-resolution hidden layer features extracted from MS and PAN are fused from patches at a local scale according to an autoencoder (AE)-like deep network. Finally, the selected multiresolution hidden layer features are classified in a supervised manner. By taking advantage of integrated shallow-to-deep and global-to-local features from the high-resolution MS and PAN images, the cross-resolution latent information can be extracted and fused in order to better model imaged objects from the multimodal representation and finally increase the classification accuracy. Experimental results obtained on three real multiresolution datasets covering complex urban scenarios confirm the effectiveness of the proposed approach in terms of higher accuracy and robustness with respect to literature methods.

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

Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions.

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.

Mixed pixels are a ubiquitous problem in remote sensing images. Spectral unmixing has been used widely for mixed pixel analysis. However, up to now, most spectral unmixing methods require endmembers and cannot consider fully intraclass spectral variation. The recently proposed spatiotemporal spectral unmixing (STSU) method copes with the aforementioned problems through exploitation of the available temporal information. However, this method requires coarse-to-fine spatial image pairs both before and after the prediction time and is, thus, not suitable for important real-time applications (i.e., where the fine spatial resolution data after the prediction time are unknown). In this article, we proposed a real-time STSU (RSTSU) method for real-time monitoring. RSTSU requires only a single coarse-to-fine spatial resolution image pair before, and temporally closest to, the prediction time, coupled with the coarse image at the prediction time, to extract samples automatically to train a learning model. By fully incorporating the multiscale spatiotemporal information, the RSTSU method inherits the key advantages of STSU; it does not need endmembers and can account for intraclass spectral variation. More importantly, RSTSU is suitable for real-time analysis and, thus, facilitates the timely monitoring of land cover changes. The effectiveness of the method was validated by experiments on four Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. RSTSU utilizes and enriches the theory underpinning the advanced STSU method and enhances greatly the applicability of spectral unmixing for time-series data.

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.

The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching.

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%.

The optical images acquired from satellite platforms are commonly multiresolution images, and converting multiresolution satellite images into full higher-resolution (HR) images has been a critical technique for improving the image quality. In this study, we introduced the generative adversarial network (GAN) and proposed a new fusion GAN (FusGAN) approach for solving the remote sensing image fusion problem. Specifically, we developed a new adversarial training strategy: 1) downscaled multiresolution images are adopted for generative network (G-Net) training, and 2) the discriminative network (D-Net) is used to adversarially train the G-Net by discriminating whether the original multiresolution images have been fused well enough. To further improve the capability of the network, we structured our G-Net with residual dense blocks by combining state-of-the-art residual and dense connection ideas. Our proposed FusGAN approach is evaluated both visually and quantitatively on Sentinel-2 and Landsat Operational Land Imager (OLI) multiresolution images. As demonstrated by the results, the proposed FusGAN approach outperforms the selected benchmark methods and both perfectly preserves spectral information and reconstructs spatial information in image fusion. Considering the common resolution disparities among intra- and intersatellite images, the proposed FusGAN approach can contribute to the quality improvement of satellite images and thus improve remote sensing applications.

Accurate land use/land cover (LULC) mapping over a large area is essential to environmentally sustainable development. Recently, the Chinese government established a new national economic zone called the Xiongan New Area, and along with the upcoming large-scale urban construction, this area will inevitably experience a dramatic LULC change, which will threaten the local ecological balance. In this article, we proposed a two-stage approach for LULC mapping in the Xiongan New Area ahead of the forthcoming dense urban construction. The first stage is to obtain base-class maps through a supervised imagery classification. Specifically, we designed a new object-based framework consisting of automatic image segmentation, pixel-based probabilistic estimation, and area-weighted probability statistics for Sentinel-2 multiresolution imagery classification. The second stage is an LULC map refinement process in which the temporal features of each land use category are extracted to refine the LULC classification. Through the implementation of the proposed two-stage approach, an LULC map containing permanent water, temporal water, natural vegetation, barren land, built-up land, and cropland categories can be produced. Through an accuracy assessment, the proposed multiresolution imagery classification method achieved the highest overall accuracy of 88.58% and an average accuracy (AA) of 87.78% compared with conventional classification methods. After obtaining the refined LULC map, we find that the current Xiongan New Area is in a less developed state, that is, cropland accounts for the highest proportion of 51.59%, which is followed by natural vegetation (22.38%) and built-up land (15.69%).

The scan-line corrector (SLC) of the Landsat 7 ETM+ failed permanently in 2003, resulting in about 22% unscanned gap pixels in the SLC-off images, affecting greatly the utility of the ETM+ data. To address this issue, we propose a spatial-spectral radial basis function (SSRBF)-based interpolation method to fill gaps in SLC-off images. Different from the conventional spatial-only radial basis function (RBF) that has been widely used in other domains, SSRBF also integrates a spectral RBF to increase the accuracy of gap filling. Concurrently, global linear histogram matching is applied to alleviate the impact of potentially large differences between the known and SLC-off images in feature space, which is demonstrated mathematically in this article. SSRBF fully exploits information in the data themselves and is user-friendly. The experimental results on five groups of data sets covering different heterogeneous regions show that the proposed SSRBF method is an effective solution to gap filling, and it can produce more accurate results than six popular benchmark methods.

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.

We develop a new geographical cellular automata (CA) modeling framework, named UrbanCA, through reconstructing the essential CA structure and incorporating nonspatial, spatial, and heuristic approaches. The new UrbanCA is featured by 1) the improvement of the CA modeling framework by reformulating relationships among CA components, 2) the development of two scaling parameters to adjust the effects of transition probability and neighborhood, 3) the incorporation of a variety of statistical and heuristic methods to construct transition rules, and 4) the inclusion of urban planning regulations and spatial heterogeneities to project future urban scenarios. To illustrate the effectiveness of UrbanCA, we calibrate a CA model using artificial bee colony (ABC) to simulate the past urban patterns and predict future scenarios in Shanghai of China. The results show that UrbanCA under different scaling parameters is comparable to CA-Markov (as a reference model) concerning the accuracy of the end-state and change simulations, and is better than CA-Markov regarding the driving factor’s ability to explain the modeling outcomes. UrbanCA provides more choices compared to existing CA software packages, and the models are readily calibrated elsewhere to simulate the dynamic urban growth and assess the resulting natural and socioeconomic impacts.

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’ as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors.

With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many existing deep-learning models focus on designing different architectures, only a few works in the RS field have focused on investigating the performance of the learned feature embeddings and the associated metric space. In particular, two main loss functions have been exploited: the contrastive and the triplet loss. However, the straightforward application of these techniques to RS images may not be optimal in order to capture their neighborhood structures in the metric space due to the insufficient sampling of image pairs or triplets during the training stage and to the inherent semantic complexity of remotely sensed data. To solve these problems, we propose a new deep metric learning approach, which overcomes the limitation on the class discrimination by means of two different components: 1) scalable neighborhood component analysis (SNCA) that aims at discovering the neighborhood structure in the metric space and 2) the cross-entropy loss that aims at preserving the class discrimination capability based on the learned class prototypes. Moreover, in order to preserve feature consistency among all the minibatches during training, a novel optimization mechanism based on momentum update is introduced for minimizing the proposed loss. An extensive experimental comparison (using several state-of-the-art models and two different benchmark data sets) has been conducted to validate the effectiveness of the proposed method from different perspectives, including: 1) classification; 2) clustering; and 3) image retrieval. The related codes of this article will be made publicly available for reproducible research by the community.

The machine learning algorithm support high efficiency in urban surface mapping based on the moderate resolution and multispectral satellite imagery. In this study, we evaluate eight widely used machine learning-based classification methods. For the specific modality of the moderate resolution and multispectral satellite imagery, nevertheless, even though many labeled datasets target for kinds of remote sensing applications are built recently, the labeled data for the moderate-resolution and multispectral image is still lacked currently. In this study, we carried out a moderate-resolution and multispectral images labeling work in two urban regions, and four-class urban surface and six-class urban surface in terms of the bio-physical and land use are labeled, respectively. Based on the labeled dataset, eight widely used machine learning-based supervised classifiers are selected for the methods evaluation. As the results obtained, the SVM, MLC and ANN achieved the highest accuracy of 88.2%, 85.4% and 84.1% in a simple four-class urban surface mapping experiment. With the increasing requirement of the remote sensing application, more advanced machine learning algorithms would be explored, and the evaluation in our study as well as the labeled dataset can provide a baseline for the future research.

This paper presents a novel multiscale superpixel-guided filter (MSGF) approach for very high resolution (VHR) remote sensing image classification. Different from the traditional guided filter (GF) classification method, the proposed method utilizes a guidance image that constructed from the superpixel segmentation image, which is capable to provide more abundant and accurate edge information of land objects presented in the image. Multiscale features are extracted by the superpixel-guided filter in order to properly model the spatial information of these objects at different scales thus to improve the classification accuracy. Experimental results obtained on a real QuickBird VHR image of Zurich urban scene confirmed the effectiveness of the proposed method.

In this paper, we present an automatic approach for change detection in a large and complex image scenario. The proposed technique takes advantages of automatic registration algorithm and change detection method that jointly measures the spatial invariant but spectral variant features in the considered bitemporal remote sensing image pair. Two classes of pseudo training samples, which associated to the change and no-change two classes, are automatically generated by analyzing the change representation information from both global and local perspectives. Finally, the robust classifier, i.e., linear support vector machine (LSVM), is used to identify the binary changes in the whole image scenario using the pseudo training samples. Experimental results obtained on a pair of real bitemporal Landsat-8 OLI images covering a large scene confirmed the effectiveness of the proposed method.

This paper proposes a novel feature-level fusion approach for burned area change detection at a fine level. The proposed approach relies on two features. The first feature is a modified normalized burn ratio (MNBR) fire index based on Landsat-8 OLI SWIR data, and the second feature is the Bright temperature (BT) based on Landsat-8 TIR data. Then two features are combined by using the gradient transfer fusion algorithm and a change detection technique to generate a fine burned area change map. A real Landsat-8 data set covering a complex fire disaster scenario is utilized to test the performance of the proposed approach. Experimental results demonstrate the effectiveness of the proposed feature-level fusion approach comparing with the reference methods in term of higher separability value and detection accuracy.

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 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.

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.

This paper focuses on solving the multi-class change detection problem in bitemporal multispectral remote sensing images. In that case, information that represented in a small number (e.g., two) of the original bands may be insufficient for the accurate identification of a few of multi-class changes. In particular, this problem becomes more difficult in unsupervised change detection cases when ground reference data is not available. In this paper, a solution is proposed by using the potential information represented in expanded features that constructed from the original spectral bands. Experimental results obtained on a real bitemporal remote sensing data set confirm the effectiveness of the proposed approach.

With the spread of Defense Meteorological Satellite Program (DMSP) data, it has played an important role in fields including urbanization and extraction of urban factors. However, the DMSP nighttime light (NTL) data is saturated in urban centers with high light intensity, resulting in not only the decrease on the DN data in nighttime light data of urban centers, but also recovering the actual light intensity differences within saturated zone. To address the above mentioned problems, we proposed a new index, named the Maximum Vegetation Adjusted NTL Urban Index (MVANUI), which combines vegetation value of Maximum Green Vegetation Fraction product (MGVF) with DMSP NTL value and reduces the effects of the NTL saturation in urban areas. Compared with the original DMSP NTL and VANUI from two aspects, that is, the capacity to distinguish and identify land features inside the saturated zone and the fitting degree of statistical factors in the social economy. Assessments on MVANUI showed that it significantly reduces NTL saturation, has comparatively higher distinguishing ability to special spatial features inside the potential saturated zone and higher relationship with population and GDP, compared with NTL and VANUI.

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.

Image sequences which obtained by close-range videogrammetry, have been widely used in the field of target identification and object tracking. The target identification is one of the key technologies and the corresponding requirement of identification precision are always at a high level in close-range videogrammetry. Therefore, this paper propose a high-precision target identification method for image sequences based on elliptical mark. The proposed approach adopts a coarse-to-fine strategy to identify elliptical mark based on pixel-level detection of Canny method and sub-pixel level identification of Zernike moments, which combines a series of constraint condition methods, thinning method and the least square fitting (LSF). In the process of coarse strategy, the constraint conditions of recursive segmentation method, morphological methods and shape feature are used to remove non-edge points caused by Canny identification. For the fine strategy, the reliable and accurate sub-pixel level edge points are identified by the thinning method and Zernike moments, and sub-pixel center position of elliptical mark are obtained by LSF method. Both simulation experiment and real experiment demonstrated that the proposed method can obtain high precision and reliability results of edge information and center position at a sub-pixel level, and its results are closer to the value by comparing the other two methods in simulation experiment.

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.

The performance of scene classification relies heavily on the spatial and structural features that are extracted from high spatial resolution remote-sensing images. Existing approaches, however, are limited in adequately exploiting latent relationships between scene images. Aiming to decrease the distances between intraclass images and increase the distances between interclass images, we propose a latent relationship learning framework that integrates an adaptive graph with the constraints of the feature space and label propagation for high-resolution aerial image classification. To describe the latent relationships among scene images in the framework, we construct an adaptive graph that is embedded into the constrained joint space for features and labels. To remove redundant information and improve the computational efficiency, subspace learning is introduced to assist in the latent relationship learning. To address out-of-sample data, linear regression is adopted to project the semisupervised classification results onto a linear classifier. Learning efficiency is improved by minimizing the objective function via the linearized alternating direction method with an adaptive penalty. We test our method on three widely used aerial scene image data sets. The experimental results demonstrate the superior performance of our method over the state-of-the-art algorithms in aerial scene image classification.

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.

A novel spectral-spatial joint multiscale approach is developed to address the multi-class change detection problem in bitemporal multispectral remote sensing images. The proposed approach is based on a multiscale morphological compressed change vector analysis (M2C2VA), which extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) while preserving more geometrical details of change targets. In particular, spectral change features are reconstructed according to the morphological analysis which exploiting the interaction of a pixel with its adjacent regions. Two multiscale ensemble strategies are proposed to integrate the change information represented at multiple scales in order to enhance the CD performance. The proposed approach is designed in an unsupervised fashion thus can be implemented without using ground reference data. A pair of real bitemporal remote sensing images is used to test the proposed approach and the obtained experimental results confirm its effectiveness.

This paper presents a novel semisupervised framework for detecting multi-class changes in bitemporal hyperspectral images. By taking advantages of the state-of-the-art unsupervised change representation technique and the advanced supervised classifiers, the proposed framework allows the generation of pseudo training samples associated with the no-change and each change class that learned from the multitemporal data and import them into the supervised classifiers. Thus multiple changes can be discriminated from the original or the transformed feature space. The proposed approach was validated on a pair of real bitemporal Hyperion hyperspectral images, and the obtained experimental results confirm its effectiveness in addressing the challenging multi-class change detection task in hyperspectral images.

Monitoring urban spatial information is vital to reveal the relationship between the human activity and environment, especially in the Chinese Silk Road Economic Belt, so as to allocate resources reasonably and realize sustainable development. To promote the remote sensing application in this field, a new method was proposed for urban built-up areas extraction mainly based on the support vector machine (SVM) classification with iterative sample refinement, combining Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data, and other auxiliary data such as Landsat images and the GlobeLand30 land cover product. Experiments were conducted by using the proposed approach for several cities in the southwest of the Chinese Silk Road Economic Belt, as classified by statistics and Landsat images. Compared with the traditional threshold dichotomy method and the state-of-the-art improved neighborhood focal statistics (NFS) method, the proposed method achieved better performance with respect to less relative error, and higher overall accuracy and Kappa coefficient.

Urban model retrieval has wide applications in the geoscience field, and it is also a very challenging research topic due to the blur and background clutter in query images and the large spatial inconsistencies between query and database images. In this study, a feature extraction and similarity metric-learning framework for urban model retrieval is proposed. In the method, the selective search voting algorithm is presented to automatically localize and segment a query object from an input image with the help of the top-ranked retrieved database images. Then, the local features of object images are extracted via sparse coding, and the global features are learned using the spatial constrained convolutional neural network. We utilize a new similarity metric to match the database images with a query object image. Finally, similar 3D models are retrieved. Both qualitative and quantitative experimental results indicate that the proposed framework can localize and segment a query object from an input image precisely and that the retrieval results are better than those of other related approaches.

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.

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.

The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computed on three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.

Modeling 3-D trees from terrestrial laser scanning (TLS) point clouds remains a challenging task for several well-known reasons, including their complex structure and severe occlusions. In order to accurately reconstruct 3-D tree models from TLS point clouds that typically suffer from significant occlusions, in this paper, a novel local structure and direction-aware approach is presented to successfully complete missing structures of trees. In this method, we first extract the coarse tree skeleton from the input point cloud, and thus, the branch dominant direction and the point density of each branch are obtained. By a skeleton-based Laplacian algorithm, the point cloud is further shrunk into a skeleton point cloud to highlight the branch dominant direction of each branch. For obtaining even more accurate point densities, a dictionary-based algorithm is utilized to learn and reconstruct the local structure. Finally, the branch dominant direction and point density are integrated into an iterative optimization process to recover the missing data. Extensive experimental results have shown that the proposed method is very robust to incomplete data sets, and it is capable of accurately reconstructing 3-D trees, which are partially, or even to a large extent, missing from the input point cloud.

With the emergence of huge volumes of high-resolution remote sensing images produced by all sorts of satellites and airborne sensors, processing and analysis of these images require effective retrieval techniques. To alleviate the dramatic variation of the retrieval accuracy among queries caused by the single image feature algorithms, we developed a novel graph-based learning method for effectively retrieving remote sensing images. The method utilizes a three-layer framework that integrates the strengths of query expansion and fusion of holistic and local features. In the first layer, two retrieval image sets are obtained by, respectively, using the retrieval methods based on holistic and local features, and the top-ranked and common images from both of the top candidate lists subsequently form graph anchors. In the second layer, the graph anchors as an expansion query retrieve six image sets from the image database using each individual feature. In the third layer, the images in the six image sets are evaluated for generating positive and negative data, and SimpleMKL is applied to learn suitable query-dependent fusion weights for achieving the final image retrieval result. Extensive experiments were performed on the UC Merced Land Use-Land Cover data set. The source code has been available at our website. Compared with other related methods, the retrieval precision is significantly enhanced without sacrificing the scalability of our approach.

Efficient presentation and recognition of on-ground objects from airborne laser scanning (ALS) point clouds are a challenging task. In this paper, we propose an approach that combines a discriminative-dictionary-learning-based sparse coding and latent Dirichlet allocation (LDA) to generate multilevel point-cluster features for ALS point-cloud classification. Our method takes advantage of the labels of training data and each dictionary item to enforce discriminability in sparse coding during the dictionary learning process and more accurately further represent point-cluster features. The multipath AdaBoost classifiers with the hierarchical point-cluster features are trained, and we apply them to the classification of unknown points by the heritance of the recognition results under different paths. Experiments are performed on different ALS point clouds; the experimental results have shown that the extracted point-cluster features combined with the multipath classifiers can significantly enhance the classification accuracy, and they have demonstrated the superior performance of our method over other techniques in point-cloud classification.

Point cloud classification plays a critical role in point cloud processing and analysis. Accurately classifying objects on the ground in urban environments from airborne laser scanning (ALS) point clouds is a challenge because of their large variety, complex geometries, and visual appearances. In this paper, a novel framework is presented for effectively extracting the shape features of objects from an ALS point cloud, and then, it is used to classify large and small objects in a point cloud. In the framework, the point cloud is split into hierarchical clusters of different sizes based on a natural exponential function threshold. Then, to take advantage of hierarchical point cluster correlations, latent Dirichlet allocation and sparse coding are jointly performed to extract and encode the shape features of the multilevel point clusters. The features at different levels are used to capture information on the shapes of objects of different sizes. This way, robust and discriminative shape features of the objects can be identified, and thus, the precision of the classification is significantly improved, particularly for small objects.

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.

The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars.

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

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.

With the rapid development of geospatial data capture technologies such as the Global Positioning System, more and higher accuracy data are now readily available to upgrade existing spatial datasets having lower accuracy using positional accuracy improvement (PAI) methods. Such methods may not achieve survey-accurate spatial datasets but can contribute to significant improvements in positional accuracy in a cost-effective manner. This article addresses a comparative study on PAI methods with applications to improve the spatial accuracy of the digital cadastral for Shanghai. Four critical issues are investigated: (1) the choice of improvement model in PAI adjustment; five PAI models are presented, namely the translation, scale and translation, similarity, affine, and second-order polynomial models; (2) the choice of estimation method in PAI adjustment; three estimation methods in PAI adjustment are proposed, namely the classical least squares (LS) adjustment, which assumes that only the observation vector contains error, the general least squares (GLS) adjustment, which regards both the ground and map coordinates of control points as observations with errors, and the total least squares (TLS) adjustment, which takes the errors in both the observation vector and the design matrix into account; (3) the impact of the configuration of ground control points (GCPs) on the result of PAI adjustment; 12 scenarios of GCP configurations are tested, including different numbers and distributions of GCPs; and (4) the deformation of geometric shape by the above-mentioned transformation models is presented in terms of area and perimeter. The empirical experiment results for six test blocks in Shanghai demonstrated the following. (1) The translation model hardly improves the positional accuracy because it accounts only for the shift error within digital datasets. The other four models (i.e., the scale and translation, similarity, affine, and second-order polynomial models) significantly improve the positional accuracy, which is assessed at checkpoints (CKPs) by calculating the difference between the updated coordinates transformed from the map coordinates and the surveyed coordinates. On the basis of the refined Akaike information criterion, the two best optimal transformation models for PAI are determined as the scale and translation and affine transformation models. (2) The weighted sum of square errors obtained using the GLS and TLS methods are much less than those obtained using the classical least squares method. The result indicates that both the GLS and TLS estimation methods can achieve greater reliability and accuracy in PAI adjustment. (3) The configuration of GCPs has a considerable effect on the result of PAI adjustment. Thus, an optimal configuration scheme of GCPs is determined to obtain the highest positional accuracy in the study area. (4) Compared with the deformations of geometric shapes caused by the transformation models, the scale and translation model is found to be the best model for the study area.

In the study, we focus on the water quality monitoring and mapping using remote sensing .The objective of the research is to develop a precise and fast way of monitoring water chemical and biochemical quality of the water resources reservation area in the upper region of Huangpu River in Shanghai, China. One scene Thematic Mapper (TM) image in summer of 2005 was acquired and the simultaneousinsitumeasurement, sampling and analysis were performed. The main methods include radiometric calibration of TM remote sensor, atmospheric correction to image data and statistical model construction. The results indicate that satellite-based estimates andinsitumeasured water reflectance have high correlation, and a statistical relationship between calibrated image data (average of 5 times 5 pixels) of TM bands and laboratory analyzed data of water samples indicated that the reflectance of TM band 1 to band 4 and organic pollution measurements such as BOD and COD had higher correlation.

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.

This article describes a methodology for establishing a general framework of the information management and aided decision making system for earthquake disaster reduction by means of a WebGIS. The system has a three-tier architecture consisting of data service, middleware, and expression service tiers. The system database is composed of the seismogeological database, the engineering environmental database, the disaster relief resource database and the analysis result database. The function components in the middleware tier include the earthquake hazard analysis, the construction vulnerability estimation, the quantifying economic losses and casualties caused by earthquakes, and the aided decision making components for post-earthquake emergency response. In order to further analyze earthquake damage data, three WebGIS analysis modules are designed on the client side, namely the foundational information module, the earthquake loss module and the emergency response module. In the information system based on this framework, the information about earthquake disaster reduction can be shared, the earthquake disaster data can be all-around analyzed, and aided decision making for post-earthquake emergency response can be efficiently made.

The generalization of line features is an important process in both traditional cartography and in many applications of geographic information systems. In This work, the parcel boundary line generalization is further studied. The uncertainties in Douglas-Peucker based line generalization algorithm is first analyzed, a line-fitting based line generalization algorithm is then proposed. In the new line generalization method, kinds of conditional equations in map generalization are therefore derived, including the length conditional equation, area conditional equation, and end-vertexes identical equation. Based on the theoretical discussion, a parcel boundary line generalization system is developed. The case studies are carried out to test the methods. The results show that the proposed uncertainty-processing model is feasible to ensure the data quality in map generalization.

Taking cadastral feature as example, the spatial object query models based on GML and XQuery are proposed. The cadastral spatial feature models based on GML are first defined. The query models of the cadastral spatial objects are then presented. The topology relationships between these objects are derived. The relation query methods and algorithms between two objects are given in detail. A GML-based cadastral GIS system is further developed based on JAVA, DOM and SAX. The proposed spatial models based on GML and the query technologies based on XQuery are tested through the system, and the results show that it is feasible to implement XQuery to query the relationships between cadastral spatial objects based on GML.

In this paper, a new error model to describe circular curve features in GIS is presented. In the model, the error of a circular curve feature can be described by two methods. In the first method, the error is described by the root mean square error in the normal direction of the circular curve. In the second method, the error is indicated by the maximum distance between the curve feature and the error ellipse. The two methods are tested through case studies, and the results are analyzed.