Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 8 references
Abstract
Abstract. The radiometric adjustment of aerial imagery is a process of very high importance considering the influences this step can generate not only on the look of the image data (white balance), but even more importantly on derived information like indices (NDVI). In comparison to the Aerial Triangulation where it is relatively straight forward to set up thresholds that need to be met in order to achieve a high-quality result, the world of radiometric adjustment is dramatically different. There is no single standard or guideline that dictates what a high-quality radiometric result will look like. Apart from these challenges there is also a rather big gap between the rich and longstanding academic work done in the field of radiometry and actual application in real-life projects. The biggest discrepancy is the usage of single images, especially when dealing with absolute radiometry approaches versus multiple thousand color balanced images in a single block in an actual production environment. In this paper, we present the advantages of utilizing reflectance measurements as a method to stabilize radiometric adjustments, as well as utilizing them as anchor to create indices like the NDVI that correspond to the value range given by literature.
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. This paper examines how to align PlanetScope and Sentinel-2 vegetation indices, focusing on the Normalized Difference Red Edge (NDRE) index, which is commonly used in precision agriculture for prescription maps. While Sentinel-2 is popular for crop monitoring, its low spatial resolution limits use in small or irregular fields. PlanetScope provides higher-resolution, more frequent imagery, but its sensor differs from the Sentinel-2, limiting compatibility with current research and tools. By testing three adjustment methods, the study shows that it is possible to align PlanetScope NDRE values with Sentinel-2: M1 (Linear Regression + Histogram Shifting + Histogram Matching), M2 (Histogram Matching), and M3 (per-band linear regression before index calculation). Two dates from 2022 were selected as representative seasonal extremes from the broader 2021–2023 dataset of 56 image pairs (Baldin, 2025), which was further analyzed through time-series methods. Resampling direction (PS→10 m, S2→3 m) minimally affects RMSE/MAE but significantly alters spatial structure and Moran’s I values; downscaling PS to 10 m decreases Moran’s I. M2 is suitable for standard applications, whereas M3 is preferable when preservation of spatial structure is important. Across the four examined scenarios, all methods reduce RMSE below the 0.07 agronomic threshold, with calibrated RMSE ranging from 0.02 to 0.05 (up to 0.06 across the full 56-pair dataset). M3’s advantage lies in how effectively it reduces spatial autocorrelation mismatch: a 43.4% reduction in Moran’s I (versus ~18.2% with M1 and M2) in the four example scenarios, and 39.5% versus 28.4% (M1) and 28.2% (M2) reduction over the full dataset.
Christian Massimiliano Baldin, V. Casella· The International Archives o...· 0 citations
Abstract. High-altitude aerial image national mosaics often exhibit visible colour and tone differences caused by atmospheric variability, illumination changes, sensor differences and post-processing workflows. These radiometric inconsistencies negatively influence both visual quality and the comparability of image data across sensors, time and campaigns. This work presents an empirical two-step colour adjustment and radiometric normalisation method for imagery acquired from 2000–3000 m altitude using a large multi-colour ground target designed to provide stable, spatially robust reference statistics. Field reflectance values are measured with a handheld spectrometer and converted to CIELAB coordinates. A global 3D similarity (Helmert) transform aligns measured image colours to ground-truth CIELAB values, followed by local residual chromatic correction using inverse distance weighting. Experiments on aerial datasets demonstrate that the method significantly reduces colour discrepancies at the calibration site.
Ivar Oveland, Steven Yves Le Moan· ISPRS Annals of the Photogra...· 0 citations
Abstract. Standardized methods for assessing the spatial resolution of airborne photogrammetric systems are essential for ensuring consistent quality in aerial imaging. This study evaluates the use of Siemens stars for empirical estimation of Ground Resolving Distance (GRD) in high-altitude aerial photo missions, based on field experiments conducted in Norway and Denmark during 2023–2025. The results confirm a consistent and expected deviation between Ground Sampling Distance (GSD) and GRD, reinforcing GRD as a critical parameter for planning, procurement, and quality assurance in airborne photo missions. The study also shows that using external reference plates to obtain reliable black and white reference values improves the reliability of GRD estimations while simultaneously enabling using smaller Siemens star. This supports the use of the GRD method as a robust and practical framework for spatial resolution assessment in aerial imagery. The fundamental objective of the project is to establish common recommendations and methodologies for aerial image quality assessment, ultimately contributing to a European-wide GRD based resolution standard. Overall, structured and transparent GRD verification is necessary to ensure consistent quality in airborne optical imagery.
Ivar Oveland, Andreas Prebensen Korsnes, E. Honkavaara et al.· The International Archives o...· 0 citations
Abstract. The Valpelline Valley, located in the northern Aosta Valley (Italy) along the Swiss border, is a typical Alpine valley shaped by glacial and fluvial processes. Characterized by a large altitudinal range (900-4000 m a.s.l.) and hosting glaciers feeding the Place Moulin reservoir, the area plays a key role in regional hydroelectric production. Since 2020, GlacierLAB has been conducting glacier monitoring activities through biannual aerial photogrammetric surveys, overcoming the logistical constraints imposed by the steep and inaccessible morphology of the valley.The surveys were performed using a medium-format camera mounted under an aircraft wing and equipped with GNSS and IMU systems. Due to the lack of synchronization between the camera shutter and GNSS receiver, georeferencing relied on Ground Control Points (GCPs), whose spatial distribution is often limited in high-mountain environments. This condition makes camera calibration a critical factor for ensuring reliable multi-temporal analysis.This study investigates the behavior of the radial distortion parameter k1 using images previously corrected for optical distortion. A multi-run bundle adjustment strategy was applied in Agisoft Metashape, including baseline configurations, global and image-wise estimation of k1, and robustness tests under different GCP setups. Statistical analyses reveal a systematic and significant dependence of k1 on the vertical camera–terrain distance.However, comparison with a theoretical atmospheric model based on the Saastamoinen formulation shows weak correlation, indicating that the observed effect cannot be attributed solely to atmospheric refraction. Instead, k1 acts as a compensatory parameter absorbing depth-dependent systematic effects related to block geometry and acquisition conditions.
M. Macelloni, N. Grasso, Alberto Cina· The International Archives o...· 0 citations
Reliable terrain understanding is a prerequisite for autonomous robot navigation. Yet, the widespread RGB-based perception can fail under low illumination, shadows, and material ambiguities. In this work we propose DRIFT, a lightweight multispectral framework that combines raw spectral bands and illumination-tolerant band-ratio representations through a dual-stream residual architecture and a differential fusion branch. Band ratios attenuate multiplicative acquisition effects (illumination/sensor gains), while the differential fusion explicitly highlights discrepancies between absolute-band and ratio-derived cues, which improves the robustness to noisy or partially unreliable spectral measurements. In the paper (i) we evaluate DRIFT on a new oil-on-soil multispectral dataset acquired using a MicaSense RedEdge-P camera mounted on an Unmanned Aerial Vehicle, and (ii) we provide an additional controlled study on water-on-grass under varying illumination and thermal perturbations (hot/cold water) to analyze NIR-sensitive effects. DRIFT consistently improves over strong baselines, while remaining compatible with edge deployment.
