Xu Huang

The generation of Digital Surface Models (DSMs) from large-format, high-resolution satellite imagery constitutes a critical component of photogrammetry and computer vision. Achieving efficient, robust, and high-quality DSM reconstruction has therefore become a prominent research focus. However, with the continuous improvement in satellite image resolution and the increasing diversity of image sources, satellite image matching—serving as the fundamental step in DSM generation—still faces significant challenges, including the uneven distribution of feature points and insufficient registration stability in large-scale imagery. To address these issues, this paper presents a refined DSM generation method for high-resolution satellite imagery, termed SAT-MAK. The framework consists of three main stages: (1) sparse matching based on MAK (Multi-type Aggregated Keypoints) extraction; (2) a density-weighted clustering matching optimization strategy; and (3) DSM generation following a conventional photogrammetric pipeline. Experiments were conducted on multiple sets of high-resolution satellite imagery, and the proposed method was compared with four commonly used satellite image 3D reconstruction algorithms. The results demonstrate that, compared with state-of-the-art methods, the proposed SAT-MAK approach improves DSM completeness by 5.29% while maintaining competitive RMSE performance, highlighting its strong potential for practical applications.

Due to the influence of factors such as satellite jitters, orbital errors, star sensor errors, and satellite clock errors, significant geometric systematic errors often exist among multi-view satellite images. This is common for multi-view, cross-orbit satellite data, where complex nonlinear systematic errors are present, making it difficult to correct them using traditional error compensation models. To achieve high-precision block adjustment, this paper proposes a direct adjustment and optimization method for Rational Function Model (RFM) parameters based on prior soft constraints. In this method, the original RFM parameters are used as prior information, which is formulated as prior information soft constraint equations in the adjustment model, aiming at effectively addressing the ill-posed problems. By directly optimizing part or all of the RFM parameters, this method can obtain stable adjustment results in scenarios of complex systematic errors. Experiments among WorldView-3, GaoFen Multi-mode, ZY-3 (Ziyuan-3), and GaoFen-7 satellite data show that, when using multi-view, cross-orbit satellite data and with sufficient and evenly distributed tie points, the proposed full-parameter RFM optimization method and the adaptive RFM optimization method can achieve the highest adjustment accuracy. On the other hand, when using in-track satellite data, the affine systematic error compensation model achieves the highest accuracy, while the adaptive RFM optimization method can achieve comparable accuracy. Therefore, the research results can be applied to intelligent processing scenarios for multi-view, cross-orbit satellite data, such as multi-temporal change detection and multi-view, cross-orbit satellite 3D modeling.

Dense Image Matching (DIM) of high-resolution satellite stereo pairs has shown wide applications in large-scale 3D reconstruction. In recent years, deep learning techniques have been widely applied in stereo matching due to their remarkable ability to extract deep features from satellite imagery. However, these methods still face challenges in scenarios such as occlusions, disparity discontinuities, texture-less regions, and repetitive patterns. To overcome these challenges, this paper proposes the Multi-Scale Chunked Attention Network (MSCA Net), an end-to-end stereo framework that improves disparity estimation accuracy by optimizing cost aggregation through a chunked processing strategy. First, multi-scale cost volumes are constructed using pyramidal feature extraction. Second, a Hier archical Spatial Aggregation (HSA) module dynamically captures multi-dimensional information in the 4D cost volume via a joint attention mechanism, enhancing initial feature aggregation. Third, a Chunked Attention Hourglass (CAH) module applies a chunk-wise attention strategy to precisely adjust features across regions, minimizing interference from redundant information. Finally, an edge-enhanced disparity refinement module improves the edge regions of the disparity map, reducing semantic distortion. Comprehensive comparisons on publicly available satellite stereo datasets demonstrate that the proposed network significantly outperforms in challenging regions, achieving end point errors (EPE) of 1.347 and 1.424 pixels on the Urban Semantic 3D and WHU-Stereo datasets, respectively—reductions of approximately 4.9% and 13.1% over current state-of-the-art methods—demonstrating superior overall performance.

The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite imagery often exhibit weak texture characteristics. This leads to serious issues in reconstructing water surface DSMs with traditional dense matching methods, such as significant holes and abnormal undulations. These problems significantly impact the intelligent application of satellite DSM products. To address these issues, this study innovatively proposes a water region DSM reconstruction method, boundary plane-constrained surface water stereo reconstruction (BPC-SWSR). The algorithm constructs a water surface reconstruction model with constraints on the plane’s tilt angle and boundary, combining effective ground matching data from the shoreline and the plane constraints of the water surface. This method achieves the seamless planar reconstruction of the water region, effectively solving the technical challenges of low geometric accuracy in water surface DSMs. This article conducts experiments on 10 high-resolution satellite stereo image pairs, covering three types of water bodies: river, lake, and sea. Ground truth water surface elevations were obtained through a manual tie point selection followed by forward intersection and planar fitting in water surface areas, establishing a rigorous validation framework. The DSMs generated by the proposed algorithm were compared with those generated by state-of-the-art dense matching algorithms and the industry-leading software Reconstruction Master version 6.0. The proposed algorithm achieves a mean RMSE of 2.279 m and a variance of 0.6613 m² in water surface elevation estimation, significantly outperforming existing methods with average RMSE and a variance of 229.2 m and 522.5 m², respectively. This demonstrates the algorithm’s ability to generate more accurate and smoother water surface models. Furthermore, the algorithm still achieves excellent reconstruction results when processing different types of water areas, confirming its wide applicability in real-world scenarios.

Mountains are an important research object for surveying, mapping, cartography, space science, and ecological remote sensing. Automatic mountain segmentation is one of the most critical techniques in large-scale mountain analyses. However, several factors limit the segmentation accuracy, such as the complexity of mountains, the noise of geospatial data, and the confusion in distinguishing non-mountainous objects with similar features. In order to improve the accuracy of mountain segmentation against these limiting factors, we impose the cloth constraint over the digital elevation model (DEM) with the underlying assumption that the mountain has a sizeable relative elevation and slope. We propose a robust mountain segmentation method with the cloth simulation constraint. The core algorithm extracts the relative elevation of the region using a cloth simulation filtering algorithm by transforming the mountain segmentation problem into an optimization problem based on the global energy function consisting of the relative elevation and slope. Experiments on a wide range of Earth and lunar elevation datasets with some of the aforementioned limitations show that the proposed method can extract complex mountain baselines, avoid the misclassification of lunar craters, and significantly improve the robustness and accuracy of mountain segmentation. Compared to three state-of-the-art methods (the Lunar Mountain Detection Method, the Landform Mask Method in SNAP™ from European Space Agency (located in Paris, France), and the Multiscale Segmentation Method in eCognition™ from Definiens Imaging (located in Munich, Germany), the F1 and IoU improved by 14.70% and 20.46% on average and 29.07% and 38.94% at most, respectively, which validates that the proposed method has a better all-around performance.

For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step “coarse-to-fine registration” algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%/26.2%, the precision by 41.0%/15.5%, the recall by 41.0%/16.0%, and the F1-score by 42.7%/15.8%. For the registration, the method can reduce the translation errors by 4.656 m/2.815 m, as well as the rotation errors by 0.538°/0.228°, which indicates its great potential in smart 3D applications.

Feature matching between high-resolution satellite stereos plays an important role in satellite image orientation. However, images of changed regions, weak-textured regions and occluded regions may generate low-quality matches or even mismatches. Furthermore, matching throughout the entire satellite images often has extremely high time cost. To compute good matching results at low time cost, this paper proposes an image block selection method for high-resolution satellite stereos, which processes feature matching in several optimal blocks instead of the entire images. The core of the method is to formulate the block selection into the optimization of an energy function, and a greedy strategy is designed to compute an approximate solution. The experimental comparisons on various satellite stereos show that the proposed method could achieve similar matching accuracy and much lower time cost when compared with some state-of-the-art satellite image matching methods. Thus, the proposed method is a good compromise between matching accuracy and matching time, which has great potential in large-scale satellite applications.

Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations. However, several factors limit the feature matching accuracy, e.g., image textures, viewing angles of stereo cameras, and resolutions of stereo pairs. To improve the feature matching accuracy against these limiting factors, this paper imposes spatial smoothness constraints over the whole feature point sets with the underlying assumption that feature points should have similar matching results with their surrounding high-confidence points and proposes a robust feature matching method with the spatial smoothness constraints (RMSS). The core algorithm constructs a graph structure from the feature point sets and then formulates the feature matching problem as the optimization of a global energy function with first-order, spatial smoothness constraints based on the graph. For computational purposes, the global optimization of the energy function is then broken into sub-optimizations of each feature point, and an approximate solution of the energy function is iteratively derived as the matching results of the whole feature point sets. Experiments on close-range datasets with some above limiting factors show that the proposed method was capable of greatly improving the matching robustness and matching accuracy of some feature descriptors (e.g., scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF)). After the optimization of the proposed method, the inlier number of SIFT and SURF was increased by average 131.9% and 113.5%, the inlier percentages between the inlier number and the total matches number of SIFT and SURF were increased by average 259.0% and 307.2%, and the absolute matching accuracy of SIFT and SURF was improved by average 80.6% and 70.2%.