Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 10 references
Abstract
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.
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
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.
Han-Gyeol Kim, Jaewon Hur· ISPRS Annals of the Photogra...· 0 citations
Accurate spatial localization of small, transient targets in low-texture aquatic environments remains a fundamental challenge in UAV-based remote sensing, where open-water surfaces often lack stable tie points, degrading exterior orientation estimation and conventional photogrammetric georeferencing. An integrated UAV framework combining DG/AAT-BA georeferencing with deep-learning-based oriented bounding box (OBB) detection was implemented for high-precision localization, validated on the Critically Endangered Yangtze finless porpoise (YFP, Neophocaena asiaeorientalis) in the Yangtze–Poyang Lake system. The georeferencing component selects direct georeferencing (DG) in open-water scenes and automated aerial triangulation with bundle adjustment (AAT-BA) in feature-rich nearshore scenes. Validation using two static verification points showed that, relative to DG, AAT-BA reduced geometric georeferencing RMSE from 2.59 to 0.62 m under straight-flight conditions and from 3.61 to 0.67 m under turning-flight conditions. For target detection, a lightweight Laplacian edge-enhancement convolution module (LapConv) was incorporated into YOLO-OBB backbones, amplifying weak-edge and low-contrast features of partially submerged targets. Across four representative YOLO-OBB models and three group-constrained partitions, LapConv consistently improved the mean mAP@0.5, with gains of 0.026, 0.024, 0.019, and 0.026 for YOLOv8, YOLO11, YOLO12, and YOLO26, respectively. Applying this framework to six UAV missions across three ecologically and hydrologically distinct subregions enabled georeferenced mapping of porpoise distributions and visualized spatial distribution characteristics during the survey period. The approach is reproducible, minimally invasive, and potentially transferable to UAV-based monitoring of other small aquatic wildlife, providing a methodological basis for fine-scale spatial surveys and subsequent habitat analysis.
Dongxu Yang, Wanbing Ren, Yanren Li et al.· Drones· 0 citations
Abstract. Identifying a distressed vessel among many ships detected in wide-area aerial imagery is a critical challenge in maritime Search and Rescue (SAR) operations. Conventional methods cannot determine which vessel matches the incident description, especially when Automatic Identification System (AIS) reports are uncertain. This study proposes an integrated framework that combines MLLM-based semantic attribute extraction with geolocation fusion to prioritize candidate vessels according to their consistency with Situation Report (SITREP) based scenarios. The method detects vessels using YOLOv8, tracks them with Deep Simple Online and Real-time Tracking (DeepSORT), and performs image-based georeferencing using onboard metadata. A Multi-modal Large Language Model (MLLM) extracts appearance/status attributes from representative vessel images, while scenario descriptions are also converted to attributes. Both sets are encoded using MiniLM embeddings. Finally, semantic similarity is fused with geolocation proximity within an Support Vector Machine (SVM) classifier to produce a probability-ranked list of candidates. Experiments using real aerial search footage demonstrate robust identification performance across a range of scenario quality levels. The correct vessel appears within the top three candidates in more than 73% of cases and within the top five in more than 91%, even when attribute extraction is affected by low resolution, illumination effects, or missing scenario information. These results show that coarse semantic cues, when combined with approximate geolocation, provide a resilient basis for identifying target vessels under high uncertainty. The proposed framework offers a practical foundation for automated SAR decision support, enabling faster and more reliable prioritization during wide-area maritime search operations.
Jeonghyo Oh, Y. Oh, Impyeong Lee· ISPRS Annals of the Photogra...· 0 citations
High-precision 3D reconstruction of objects with complex surfaces, such as ancient architecture and detailed artworks, requires close-range image acquisition, which remains challenging for Unmanned Aerial Vehicle (UAV) systems. The operational proximity of current UAV workflows is often insufficient to capture fine geometric and textural details, limiting high-fidelity digitization. This paper presents a georeferenced NeRF-based UAV acquisition framework for automated waypoint planning and supervised close-proximity execution. The core of the framework is a path-planning module that operates on a metric geometric prior established through Geographic Neural Radiance Fields (Geo-NeRF), which denotes a georeferenced NeRF modeling pipeline rather than a new NeRF architecture or loss function. By generating waypoints directly on this neural representation and optimizing the flight path via a nearest-neighbor strategy, the proposed framework supports close-proximity image acquisition for static targets under controlled conditions. Empirical validation demonstrates improved close-range flight proximity, photographic accuracy, and 3D reconstruction fidelity compared with the evaluated baselines.
Haoyu Liu, Yizhi Zou, Lu Yang et al.· Drones· 0 citations
Abstract. High revisit-rate Synthetic Aperture Radar (SAR) constellations generate large volumes of imagery that require consistent geolocation accuracy to support applications such as change detection and interferometry. However, variations in orbit determination, attitude knowledge, and external factors such as Global Navigation Satellite System (GNSS) interference can introduce geolocation errors that vary across acquisitions, making large-scale validation challenging. This study presents an automated approach to detect and quantify geolocation offsets in ICEYE SAR imagery by aligning orthorectified scenes with reference images using feature-based matching and correlation-based refinement. The method is validated against independently derived absolute geolocation measurements from corner reflector calibration sites in the United States, Canada, Australia, and Poland. Evaluation across 726 acquisitions demonstrates strong agreement with reference measurements, achieving an overall root-mean-square error (RMSE) of 1.39 m, with RMSE values of 1.18 m for Spotlight mode and 1.93 m for Stripmap mode. Operational applicability is demonstrated through large-scale acquisition campaigns, including nationwide Stripmap coverage over Japan and coherent image stack analysis. The results show that the proposed method can reliably estimate geolocation offsets, detect anomalies, and monitor geometric consistency across large SAR archives, providing a practical and scalable solution for automated geolocation quality control in micro-satellite SAR constellations.
A. Johnsy, Eyrin Kim, Qiaoping Zhang et al.· The International Archives o...· 0 citations