Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 2 citations· 1 references
TL;DR
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.
Abstract
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.
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
In optical remote sensing 3D reconstruction, high-resolution satellite stereo matching is a critical task, yet it is challenged by extreme imaging geometries, texture-less and repetitive patterns, occlusions, and scene variations caused by spatio-temporal heterogeneity. To address these issues, we propose IFMA-Stereo, an innovative binocular disparity estimation method that leverages a monocular depth foundation model. Our approach constructs a multi-scale spatial information pyramid to jointly integrate the foundation model with a disparity extraction network. At the feature level, an attention interaction mechanism captures multi-dimensional contextual dependencies and transforms general scene understanding priors into long-range associative features suitable for stereo cost volume construction. At the pixel level, a cyclic iterative refinement module embeds depth information from the foundation model throughout the iteration process and performs joint optimization, enhancing the model’s adaptability in geometrically complex regions. Experiments on the US3D and GaoFen-7 datasets demonstrate that IFMA-Stereo achieves superior performance in challenging areas (texture-less regions, disparity discontinuities, repetitive patterns) and effectively mitigates prediction errors caused by spatio-temporal heterogeneity, albeit at the cost of increased inference time compared to baseline methods. Quantitatively, the method achieves an end-point error (EPE) of 1.347 and a D1 error of 7.26% on the US3D dataset, and an EPE of 1.585 and a D1 error of 13.41% on the GaoFen-7 dataset. Notably, the method also yields precise predictions for unseen urban areas, indicating strong generalization. These results confirm that IFMA-Stereo achieves state-of-the-art accuracy in remote sensing disparity estimation.
Co-registration underlies nearly every multi-temporal and multi-sensor use of optical satellite imagery, and operational products still carry documented offsets well above the fraction-of-a-pixel scale at which change detection, time series, and data fusion degrade. Real image pairs differ along several axes at once (sensor response, scene content, viewing geometry, resolution, mosaic seams), and the last of these is not a single global motion. Existing tools embed a motion model and constants tuned to their development data; a pair that fits is registered precisely, while one that does not either fails to match or returns a result wrong by tens of pixels with no failure reported. Learned matchers add a GPU requirement and carry no accuracy guarantee outside their training distribution. We present SCDF (self-calibrating displacement fields), a training-free, GPU-free estimator whose motion model is the dense per-pixel displacement field itself, so no scene motion falls outside the model. A single predict--measure--filter loop runs over a resolution pyramid: the accumulated field predicts where each patch of the moving image falls in the reference, RootSIFT matching and a correlation pass measure the displacement there to sub-pixel precision, and filters whose thresholds are all calibrated on the image pair itself decide what survives. One configuration, with no per-dataset tuning, processes full $8192^2$ scenes on a single CPU core. On 584 constructed-ground-truth pairs built from real Sentinel-2, Landsat-8/9, and NAIP imagery, against seven classical baselines and two zero-shot pretrained matchers, SCDF registers every pair with zero failures, reduces the best baseline's real-pair median end-point error from 6.83 to 4.17m, and cuts its 90th percentile from 17.8 to 7.77m.
Shoukun Sun, Zhe Wang, S. Salati et al.· 0 citations
Accurate refinement of Rational Polynomial Camera (RPC) models is essential for high-quality satellite image geolocation. In ground control point (GCP)-free multi-view pipelines, this refinement is commonly performed through bundle adjustment from automatically extracted image correspondences. However, conventional RPC bundle adjustment pipelines rely on handcrafted feature matching, which becomes unreliable in multi-date collections affected by seasonal, illumination, and land-cover changes. We propose 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. This reduces redundant and error-prone matching while preserving the connectivity of the matching graph. Experiments on seasonally diverse WorldView-3 images show that our pipeline improves GCP-free relative RPC refinement over open-source baselines, achieving lower geometric consistency errors while substantially reducing matching time on collections with 39-42 views. By making RPC refinement more robust to diachronic appearance variation, our approach enables more effective use of multi-date satellite imagery.
Roger Marí, El'ias Masquil, Xavier Bou et al.· 0 citations
Abstract. This study presents a self-calibration method for optical Earth observation satellites equipped with matrix sensors. Precise geolocation of each pixel in a satellite image requires accurate modelling of the acquisition geometry, typically achieved through refinement that corrects the geometry using measurements such as correspondences between image pixels and ground coordinates, or between pixels in different images. A critical aspect of this modelling involves the sensor's internal geometry, which defines the line-of-sight (LOS) vector for each pixel in the focal plane. The calibration method proposed in this article eliminates the need for exogenous data (e.g., higher-resolution satellite images or airborne sensor imagery) by relying solely on a set of acquisitions in a specific configuration. The method is evaluated and validated using CO3D imagery. Several evaluation criteria were developed for this purpose, including the reliability of the refinement process, the quality of tie-point intersections, inter-site result comparisons, and alignment with absolute references. This paper does not include the in-orbit performances due to confidentiality agreement.
Guillaume Laurent, Alice Latourte, Fabrice Buffe et al.· The International Archives o...· 0 citations
SeasonStereo is proposed, a scalable framework that addresses disparity estimation from diachronic satellite images by training on synthetic image pairs with controlled seasonal appearance variation, while leveraging zero-shot geometric priors from foundation models.
Álvaro Díaz-Laureano, Roger Mar'i, El'ias Masquil et al.· 0 citations
