Jul 2026· ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XI-1-2026, pp. 159-166· 0 citations· 7 references
TL;DR
A two-stage framework that utilizes AI matching technologies and open access data to automatically correct satellite images lacking georeferencing information is proposed, providing a practical, accessible, and automated geometric correction solution for general users.
Abstract
Abstract. Geometric correction of satellite images is an essential pre-processing step for accurate geospatial analysis, but non-experts often face practical limitations because detailed sensor models and Ground Control Point data are not readily accessible. Traditional methods rely on physical sensor models or the Rational Function Model (RFM) using vendor-provided Rational Polynomial Coefficients (RPC). However, this information is often unavailable or lacks sufficient accuracy. This paper proposes a two-stage framework that utilizes AI matching technologies and open access data to automatically correct satellite images lacking georeferencing information. In Stage 1, a coarse Affine correction is executed using SuperPoint and LightGlue with an open basemap (Sentinel-2). In Stage 2, precise corresponding points are extracted through patch-based hierarchical LoFTR matching, and 3D GCPs are generated utilizing the SRTM. Subsequently, sensor-independent RPC are robustly estimated through the rpcfit library, and the final geometrically corrected image is generated through resampling. This framework was verified by applying it to 4.8m resolution BlueBON satellite images that lack georeferencing information. In seven experimental regions with diverse geographical characteristics, an average Root Mean Square Error (RMSE) of 8.050m (1.68 pixels based on BlueBON resolution) referenced to the Sentinel-2 basemap, and an average of 9.02m (1.88 pixels) referenced to Google Maps, was achieved. This result demonstrates that it is possible to precisely correct 4.8m medium-resolution images using a 10m open basemap, providing a practical, accessible, and automated geometric correction solution for general users.
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
High-resolution optical satellite constellations continuously generate massive volumes of remote sensing imagery, making automatic, ground-control-point-free (GCP-free) geometric positioning accuracy assessment increasingly important for ensuring the quality of downstream applications. However, conventional GCP-free inspection methods based on local feature matching often exhibit limited robustness under large initial positioning errors, weak-texture regions, cloud contamination, and temporal appearance variations, resulting in poor generalization across large-scale production scenarios. To address these challenges, this paper proposes a geometry-constrained framework that integrates Rational Polynomial Coefficient (RPC) prior constraints, coarse-to-fine registration, adaptive match-density-based block selection, hierarchical geometric verification, and a geolocation residual confidence measure into a unified automatic quality inspection pipeline. The framework leverages LoFTR for dense feature matching, but its principal contribution lies in the system-level integration and operational design for large-scale industrial satellite image production. Extensive experiments on multi-satellite and multi-scene datasets from the Jilin-1 satellite series show that the proposed method achieves a median positioning error below 2 m, an Average Precision (AP) improvement of 0.53 over the baseline, and nearly perfect accuracy on the evaluated test set for confidence thresholds above 0.5. The framework has also been deployed in the operational production system of multiple commercial Jilin-1 missions for more than six months, demonstrating its effectiveness, robustness, scalability, and practical applicability for large-scale optical satellite imagery.
Jia-Ming Cui, Wei-Bin Wang, Li-Ming Fan et al.· Remote Sensing· 0 citations
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.
Jiyong Kim, Shuang Song, Rongjun Qin· The International Archives o...· 0 citations
This work proposes an appearance-aware RPC refinement pipeline that combines learned local feature matching for season-invariant correspondences with global image descriptors for selecting visually compatible image pairs, which reduces redundant and error-prone matching while preserving the connectivity of the matching graph.
Roger Marí, El'ias Masquil, Xavier Bou et al.· arXiv.org· 0 citations
Abstract. The 3D documentation of complex scenes—characterized by restricted spaces, irregular geometries, and poor lighting—remains a significant challenge in cultural heritage. This study proposes a rapid data acquisition methodology based on the multi-sensor fusion of Terrestrial Laser Scanning (TLS) and Spherical Photogrammetry (SP). The approach was validated in two distinct complex environments: an ancient Egyptian rock-cut tomb (QH36, Aswan, Egypt) and a natural Iberian sanctuary cave (Cueva de la Lobera, Jaén, Spain). The methodology uses TLS to establish a high-precision geometric backbone, achieving registration errors below 0.5 cm. By extracting Ground Control Points (GCPs) directly from the TLS point cloud, the reliance on traditional total station surveying was significantly reduced, enhancing fieldwork efficiency. SP was implemented to obtain realistic textures and to support geometry by using a 360-degree multi-camera with integrated LED lighting, providing full spherical coverage and high-resolution textures. Results indicate that SP is at least six times faster than conventional photogrammetry. Furthermore, the use of TLS-derived meshes enabled advanced digital masking to remove non-interest objects (e.g., archaeological equipment) from the final models. While conventional photogrammetry remains the benchmark for fine architectural details, this research demonstrates that the TLS-SP fusion is the most viable solution for the rapid, high-accuracy documentation of constrained heritage sites. This hybrid workflow ensures geometric integrity while drastically reducing acquisition times, providing a robust framework for future archaeological and conservation projects.
A. Mozas-Calvache, José Luis Pérez-García, J. M. Gómez-López et al.· The International Archives o...· 0 citations
Geometric accuracy assessment is a fundamental prerequisite for the application of high-resolution optical satellite imagery, yet it remains particularly challenging in the absence of ground control points. This study proposes a consistency-driven accuracy assessment framework for ZY-3 satellite imagery that systematically bridges the conventional separation between block adjustment and accuracy evaluation. The framework is built upon a unified geometric error model that accommodates both inter-image consistency assessment and absolute accuracy evaluation within a common mathematical formulation. Recognizing the inherent uncertainties of publicly available reference data, a hierarchical validation chain is established: the Google Earth (GE) and Shuttle Radar Topography Mission (SRTM) datasets are first validated against WorldView imagery to bound their intrinsic errors, and subsequently employed as references for ZY-3 accuracy assessment. This design enables the simultaneous evaluation of planimetric accuracy, vertical accuracy, and inter-image consistency without reliance on ground control points. Experimental validation conducted on 1368 ZY-3 scenes covering a study area of 1.9 million km2 yields a planimetric RMSE of 3.33 m and a vertical RMSE of 4.27 m. The results demonstrate that higher inter-image consistency empirically correlates with improved absolute positioning accuracy, and that the proposed framework not only reliably evaluates geometric quality but also provides diagnostic insights that can inform proactive adjustment strategies. The proposed method thus offers a practical and transparent solution for the geometric accuracy detection and enhancement of large-area ZY-3 satellite imagery.
Ying Zhao, Haitao Zhao, Zhizhong Kang et al.· Remote Sensing· 0 citations
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