Conference Agenda

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.

Star centroid localization accuracy fundamentally limits spacecraft attitude determination precision. Existing methods face a critical accuracy-efficiency trade-off: traditional intensity-weighted approaches achieve computational efficiency (<1 ms/star) but suffer from poor noise robustness, while Gaussian fitting and deep learning methods provide high accuracy at prohibitive computational costs. We address this fundamental limitation by developing a principled Bayesian Multi-Scale Adaptive Iteratively Weighted (BMAI) centroid localization algorithm that achieves high accuracy approaching theoretical limits while maintaining real-time computational efficiency. The algorithm integrates four key technical contributions: (1) SNR-adaptive window extraction with robust threshold estimation, (2) regularized iteratively weighted framework with proven convergence properties, (3) multi-scale fusion with SNR-dependent weighting, and (4) gradient-based refinement to mitigate systematic bias. Rigorous theoretical analysis establishes convergence guarantees, derives error bounds, and evaluates Cramér-Rao Lower Bound (CRLB) efficiency. Comprehensive evaluation on 16,500 synthetic star images across six diverse imaging scenarios demonstrates that under high-SNR conditions (SNR >25, n=2,000), BMAI achieves mean RMSE of 0.0120 pixels (95% CI: [0.0116, 0.0124] pixels), representing a 98.6% relative improvement over intensity-weighted centroiding (0.857 pixels), 35.8% improvement over Gaussian fitting (0.0187 pixels) and 95.3% improvement over CNN methods(0.2566 pixels). The algorithm maintains computational efficiency of 0.89ms per star—8.7× faster than Gaussian fitting—while achieving CRLB efficiency of 79.2%. Robustness analysis demonstrates stable performance across SNR range 3-100 with graceful degradation under challenging conditions. The BMAI algorithm fundamentally resolves the accuracy-efficiency trade-off in star centroid localization through principled Bayesian inference and multi-scale processing.

This paper presents a systematic investigation of the PhaseOne native IIQ format for drone and aerial cameras (in particular the recent iXM-RS250 and the iMX-GS120), focusing on the influence of different compression levels on geometric, radiometric and computational aspects of the photogrammetry pipeline. The aim of the presented research and experiments is to demonstrate the actual quality of these (compressed) images for photogrammetric purposes.

Small format multi-head cameras are becoming available and can be flown on light drones to provide simple access to oblique and nadir views of built-up areas. A number of missions with different parameters (flying height, etc.) are investigated to understand the trade-offs in applying those sensors and question the established accuracy laws. We investigate and quantify the ability to completely cover the facades using those sensors in the different scenarios.

In recent years, commercial small optical satellites, e.g., Skysat, BlackSky, and PlanetScope, have become widely used for a variety of Earth remote sensing applications, providing high-resolution images with sub-meter resolution. They are operated in a constellation of multiple satellites, which compensates for the spatial and temporal limitations of traditional satellite observations. Moreover, their data acquired during stereo viewing have been experimentally used to generate digital surface models (DSMs). The CE-SAT-IE is a small optical satellite developed by Japan’s commercial company Canon Electronics Inc. (CE) and was launched on 17 February 2024, by Japan Aerospace Exploration Agency’s (JAXA’s) H3 launch vehicle test flight no.2. It is equipped with an optical frame sensor derived from a commercial off-the-shelf (COTS) camera Canon EOS R5. The ground sampling distance (GSD) is 0.8 m with a scene size of 6.5 km × 4.3 km. The calibration and validation of the sensor are being conducted in collaboration between CE and JAXA, drawing on JAXA’s extensive experience with past satellites. The geometric and radiometric performance of the sensor is analysed in detail, and the results will be used for its subsequent mission, which may involve a constellation for stereo observation to generate high-quality DSMs. This paper reports initial results for geometric calibration and validation of the sensor using ground control points (GCPs) and the experimental generation of DSMs from stereo observation images using the calibrated parameters.

This paper presents a novel hybrid calibration pipeline that jointly estimates the spatial and temporal alignment between a handheld laser scanner and a smartphone camera without any hardware synchronization. The method combines deep-learning-based target detection with classical geometric calibration using 2D-3D correspondences derived from black and white hourglass planar targets. Target centers are precisely localized in both the RGB images and the 3D point cloud using a symmetric templatematching scheme, enabling robust solution of the perspective-n-point (PnP) problem for spatial calibration.

To address the lack of hardware synchronization, we introduce a temporal calibration method that exploits geometric correspondences between rendered intensity images and camera frames.

On a Lixel L2 Pro scanner with a Huawei P20 Pro camera, the pipeline achieves a median Reprojection error of 0.76 px for static calibration and 2.19 px across 91 dynamic evaluations. The approach enables accurate image-pointcloud fusion for scanners without syncronisation interfaces and provides a foundation for colorization, image analysis, and redensification of laser data.