Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 7 references
Abstract
Abstract. Satellite imagery offers a distinct advantage in Earth observation by providing expansive coverage and enabling the monitoring of inaccessible regions without physical on-site intervention, serving as a significantly more cost-effective and scalable alternative to traditional aerial or ground-based surveys. The task of 3D reconstruction from multi-view satellite images has therefore been a pivotal point of research at the intersection of photogrammetry and remote sensing. Recently, novel-view synthesis techniques such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have accelerated the accuracy and speed of topographic modeling. Among these, Earth Observation Gaussian Splatting (EOGS) has emerged as a state-of-the-art approach by adapting 3DGS to handle the unique geometric and radiometric characteristics of satellite data, including Rational Polynomial Coefficients (RPCs) and varying solar conditions. However, the standard EOGS pipeline relies on stochastic initialization, where Gaussians are distributed uniformly within a volumetric bounding box, leading to high computational overhead and dependency on aggressive pruning that can inadvertently remove critical geometric features, particularly in areas with complex urban structures. To address these limitations, we propose Bundle-Adjusted Initialization for Earth Observation Gaussian Splatting, which leverages sparse point clouds from bundle adjustment as geometric priors for Gaussian initialization. Combined with an adaptive densification strategy, our method achieves faster convergence and improved DSM accuracy on the DFC2019 dataset compared to the EOGS baseline.
In the era of satellite constellations, multi-view optical satellite imagery is pivotal for Earth Observation (EO) and high-quality Digital Surface Model (DSM) reconstruction. Although feed-forward 3D foundation models have transformed computer vision, their deployment in satellite remote sensing is inherently constrained by the structural discrepancy between implicit perspective assumptions and explicit orbital pushbroom geometry. This geometric incongruity is further compounded by pronounced view-set heterogeneity. We present EO-VGGT, a framework that adapts a frozen perspective-driven model to orbital observations via explicit physical geometry embedding.First, the Geometry-Correlation Constrained Selection (GCCS) strategy prunes sub-optimal observations by balancing geometric diversity and radiometric consistency to optimize the input sequence. Second, a Sensor-Ray Encoder (SRE) parameterizes pixel-level pushbroom lines of sight derived from the Rational Function Model (RFM) into high-dimensional space-geometric tokens, reconciling the mathematical discrepancy between central projection and orbital kinematics. Third, a lightweight Ray-Pointing-Aware Adapter (RPAA) employs gated residual blocks to integrate these tokens directly into the frozen transformer backbone. Our findings underscore that integrating explicit physical geometry with optimized view selection is essential for robust feed-forward satellite 3D reconstruction.
Abstract. In the initial response to wildfires, securing rapid and accurate geographic information is essential. However, helicopter imagery acquired on-site often lacks precise sensor metadata, such as camera pose and internal parameters, making the application of georeferencing difficult. In particular, obliquely captured wildfire imagery presents additional registration challenges due to severe viewpoint changes, scale variations, and low-texture environments. This study proposes an automated georeferencing pipeline capable of operating under these constraints. The proposed method consists of five stages: preprocessing, image retrieval, feature extraction and matching, Exterior Orientation Parameters (EOP) estimation, and orthomosaic generation. An initial Area of Interest (AOI) is defined using inaccurate initial position data, and the Region of Interest (ROI) within the reference map is obtained through a ResNet50-based image retrieval approach. Subsequently, virtual Ground Control Points (GCPs) are generated through deep learning-based feature matching. Elevation data is then assigned using a Digital Elevation Model (DEM), and EOP are estimated via Perspective-n-Point (PnP) and RANSAC algorithms. Intermediate frames are initialized via interpolation and refined through bundle adjustment to produce the final orthomosaic. Experimental results demonstrated that utilizing SuperGlue and LightGlue complementarily increased the number of successfully georeferenced intervals from 5 to 9. Furthermore, a minimum RMSE of 28.30 m was achieved in the most accurate interval. This method proves that by automating the feature-based georeferencing process, practical geographic information can be rapidly provided for initial disaster response, even in sensor-limited environments.
Seongyun Kim, Jeonghyo Oh, J. Cheon et al.· The International Archives o...· 0 citations
Abstract. Satellite imagery acquired over large areas from multiple viewpoints introduces subtle geometric misalignments that degrade the quality of derived products such as Digital Surface Models (DSMs). This paper presents a global block adjustment workflow designed to correct these errors across overlapping stereo acquisitions from the “Constellation Optique 3D” (CO3D) constellation, which captures Earth's surface at 50 cm resolution. The proposed pipeline operates in three stages: individual acquisition refinement using Space Reference Points (SRPs) as Ground Control Points; tie point extraction between overlapping scenes through two-pass image correlation; and a weighted global spatio-triangulation simultaneously optimizing attitude biases, attitude drifts, and per-satellite magnification parameters. Applied to a large stereo acquisition dataset over the Aorounga crater, Chad, the method demonstrates strong geometric performance. The results highlight that careful parameterization — combining observation weighting, n-tuple point filtering, and per-satellite sensor refinement — is key to producing accurate, geometrically consistent large-scalemosaics from bi-satellite stereo imagery. This paper does not include the in-orbit performances due to confidentiality agreement.
Michaël Erblang, Emelyne Saulnier, Guillaume Laurent et al.· The International Archives o...· 2 citations
Abstract. The proliferation of continuous Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) has shifted the paradigm of 3D aerial reconstruction from relying solely on geometric stereo matching to inverse rendering optimization. However, while these emerging rendering-based frameworks excel in synthesizing photo-realistic novel views, their capability to extract accurate surfaces in complex aerial scenarios remains ambiguous compared to traditional methods. To establish a clearer understanding, this study presents a comprehensive evaluation of five representative frameworks spanning traditional Structure from Motion (SfM), purely Signed Distance Field (SDF) representations, unstructured 3D Gaussians, hybrid voxel-Gaussians, and strictly explicit sparse voxels. By systematically standardizing identical computational environments, inputs, and unified mesh-extraction pipelines on both real-world airborne LiDAR datasets and synthetic cityscapes, we assess their performance regarding visual fidelity, geometric accuracy, and resource efficiency. The experimental results reveal that while traditional MVS produces the highest overall geometric precision by strictly enforcing multi-view rigid geometry, it is prone to failures in texture-less regions. Among rendering-based representations, a fundamental trade-off exists: highly flexible, unstructured 3DGS achieve highest visual scores but degrade the underlying geometric surfaces; conversely, explicitly structured techniques demonstrate distinct superiority in regularizing topological coherence and floating artifact suppression. Furthermore, we observe that integrating structured voxels avoids the severe memory bottlenecks associated with extracting geometries from chaotic unorganized primitives. These empirical findings emphasize that for large-scale aerial photogrammetry, integrating explicit spatial structuralization into differentiable rendering pipelines is imperative for achieving scalable operations and bridging the geometric accuracy gap with traditional methods.
Shihan Chen, Zhaojin Li, Qingsong Yan et al.· The International Archives o...· 0 citations
Abstract. Three-dimensional (3D) reconstruction from satellite imagery is a critical research topic in the fields of remote sensing and geoinformation science. Although 3D Vision Foundation Models (3D VFMs) have demonstrated remarkable performance in reconstructing natural scenes, their capability to handle high-resolution satellite imagery has not been systematically evaluated. This study presents a comprehensive assessment of seven representative 3D VFMs for satellite-based 3D reconstruction and integrates four point-cloud alignment strategies. Rigorous comparisons were conducted against high-precision LiDAR-derived Digital Surface Models (DSMs) using two publicly available multi-view satellite datasets–WHU-TLC and MVS3D. The results show that Depth Anything V2 (DAV2) combined with an affine alignment strategy achieves the best overall performance among the evaluated methods. On the MVS3DM dataset, the reconstructed DSM achieves a Median Absolute Error(MedAE) of 1.693 m, a Root Mean Square Error (RMSE) of 3.649 m, and competitive reconstruction accuracy compared with several traditional photogrammetric pipelines. In contrast, on the lower-resolution WHU-TLC dataset, all 3D VFMs exhibited notable performance degradation, and the reconstructed results showed limited practical value, revealing persistent generalization challenges for current models in low-resolution scenarios. Overall, this study systematically quantifies the performance of 3D VFMs in satellite image-based 3D reconstruction, confirming their strong potential for high-resolution satellite applications and providing valuable insights for enhancing model robustness and generalization across complex urban and low-resolution environments.
Liupeng Su, Yuhao Ye, Han Hu et al.· ISPRS Annals of the Photogra...· 0 citations
Remote sensing has become a core technology for environmental and climate monitoring, supported by expanding sensor constellations, advanced processing capabilities, and coordination frameworks established by the European Space Agency (ESA), Global Earth Observation System of Systems (GEOSS), and Committee on Earth Observation Satellites (CEOS). Ensuring consistency across missions requires robust geometric and radiometric calibration and validation. However, traditional reliance on ground control points (GCPs) is limited by sparse global coverage, temporal instability, and dependence on surveyed accuracy. While alternative geospatial datasets, including satellite and aerial imagery, Light Detection and Ranging (LiDAR) point clouds, and vector databases, can serve as references, challenges remain in data access, automation, and cross-sensor applicability. This study proposes a generative adversarial network (GAN)-based approach to generate geometrically consistent image chips from vector maps. Two models were trained at 50 cm and 10 m resolution within the ESA-supported Generative Ground Control Point (GenCP) study, using Sentinel-2 and very-high-resolution RGB imagery. The generated GenCP image chips are evaluated using image similarity (radiometric consistency), as well as geometric and model-performance metrics. The results demonstrate their suitability for automated Cal/Val workflows and their potential as scalable, fit-for-purpose reference datasets.
Elodie Guasch, I. Yalcin, S. Saunier et al.· Remote Sensing· 0 citations