Image-based localization in GNSS-denied environments is critical for UAV autonomy. Existing state-of-the-art approaches rely on matching UAV images to geo-referenced satellite images; however, they typically require large-scale, paired UAV–satellite datasets for training. Such data are costly to acquire and often unavailable, limiting their applicability. To address this challenge, we adopt a training paradigm that removes the need for UAV imagery during training by learning directly from satellite-view reference images. This is achieved through a dedicated augmentation strategy that simulates the visual domain shift between satellite and real-world UAV views. We introduce CAEVL, an efficient model designed to exploit this paradigm, and validate it on ViLD, a new and challenging dataset of real-world UAV images that we release to the community. Our method achieves competitive performance compared to approaches trained with paired data, demonstrating its effectiveness and strong generalization capabilities.

Classical approaches to the rectification of a single image of a product, without stereo correspondences, require spatial landmarks. These landmarks, constructed from high-contrast elementary shapes that can be detected with simple algorithms, are highly conspicuous. To rectify complex deformations, one can use chessboard patterns of markers with elements that break quadrilateral symmetry, such as the three eyes of a QR code. However, these marker boards are even more conspicuous than a single marker. In traceability applications, only one site of marking is used, limiting the complexity of the surface on which it can be read, and exposing the mark to deidentification attacks for diversion of the product to a grey market. We introduce a method for constructing stealth and robust fiducial markers that can be displayed across a surface, limiting exposure to marker tampering for product deidentification. These markers, which we refer to as self-rectifying textures, can be used to rectify complex deformations by solving an inverse problem rather than relying on pixel correspondences of conspicuous landmarks. These stealth textures place fiducial markers in the autocorrelation of the image. In this way, crops of the deformed texture can be rectified using only these spatially invariant statistical properties. Affine transformations of an image correspond to linear transformations of the autocorrelation, without phase component. Exploiting this fact, self-rectifying textures enable the local estimation of the differential of a planar deformation by identifying landmarks in the autocorrelation image, such as peaks, whose locations in the fronto-parallel view of the texture are known. The translation component can be recovered independently via phase correlation. A rectifying map, modulo translations, can also be fit directly to local observations of the differential of the deformation, without access to the rectified texture or need for phase correlation. Self-rectifying textures can be used for communication, watermarking, authentication, surface identification, calibration, and geometry processing.

Point cloud visibility estimation is fundamental as it is useful for many computer vision applications including surface reconstruction, 3D segmentation from paired images and point densification. Previous works showed outstanding results on simple object and outdoor datasets. However, unlike the previously studied scenes, the most challenging environments are those providing a high amount of object points in the same direction, typically in complex indoor scenes. In this kind of environments, due to the lack of real data ground truth, quantitative analysis are either missing or based on simulated data. In this work, we present IRIS-VIS (Industrial Room In Saclay - VISibility), a new dataset for point visibility estimation in an indoor environment. It is a high complexity scene due to the large variety in the shape, size and orientation of the objects. To our know-ledge, this is the first dataset on real indoor data providing a dense LiDAR station-based point cloud along with a well-fitted CAD model. The latter is useful to compute automatically, quickly and accurately the visibility from any given viewpoint, enabling evaluations under infinite conditions. We propose new metrics for the visibility estimation task and evaluate state-of-the-art methods in both sparse and dense conditions with the proposed dataset.

This paper introduces a new method for authenticating engravings, crucial to fight counterfeiting and related issues in industries like luxury brands. The approach is based on extracting from the engravings natural minutiae, inspired by human biometrics, called (n, n)-modules. These modules, representing small sub-matrices of the printed pattern, that vary in appearance due to factors like material characteristics and engraving procedure. To create a reliable fingerprint, we proposed a voting system, with each vote being a comparison between two (n, n)-modules. This method leverages of the fact that the defects of an engraving or printing are difficult to reproduce and are observable on the scale of a printed or engraved point. We evaluate two ways of comparing the minutiae: a standard one using Euclidean distance and a deep learning-based one employing a convolutional neural network. The process delivers high accuracy and recall in authenticating engravings and shows robustness under various lighting conditions and levels of image blur.

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically point source methane leaks using high resolution hyperspectral images from the PRISMA satellite. We propose an improvement of the classical matched filter method by using an adjustment coefficient. We introduce this new method under the name: Model Adjusted Matched Filter (MAMF). We show that the MAMF method reduces the fraction of false detections compared to the Matched Filter (MF) and the Adaptive Cosine Estimator (ACE) without preventing the detection of plumes. To validate the method, we use a dataset of manually annotated plumes on PRISMA images. We then show that our method outperforms the matched filter and the adaptive cosine estimator in terms of F1 score.

We propose a new method for the geolocalization of Un-maned Aerial Vehicles (UAV) in environments without Global Navigation Stallite Systems (GNSS). Current state-of-the-art methods use an offline-trained encoder to compute a vector representation (embedding) of the current UAV’s view, and compare it with the pre-computed embeddings of geo-referenced images in order to deduce the UAV’s position. Here, we show that the performance of these methods can be greatly improved by pre-processing the images by extracting their edges, which are robust to seasonal and illumination changes. Moreover, we also show that using edges improves the robustness to orientation and altitude errors. Finally, we present a confidence criterion for localization. Our findings are validated using synthetic experiments.

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically point source methane leaks using high resolution hyperspectral images from the PRISMA satellite. We use a variation of the Matched Filter (MF) called the Adjusted Spectral Matched Filter (ASMF) to detect methane plumes in satellite images. To remove false positives, the detected plumes are confirmed by comparing their orientation to the wind direction extracted from the standard meteorological reanalysis product ERA5. The ASMF reduces the fraction of false detections compared to the MF and without preventing the detection of plumes. To validate the method, we use a recently proposed dataset of manually annotated plumes on PRISMA images. We also compare our detection rate to the detection rate of methods using deep learning or the standard matched filter. We then show that our method outperforms those methods in terms of F1 score.

Shape from shading (SfS) enables 3D reconstruction of stockpiles from a single image. However, this method requires proper boundary conditions to work properly. Obtaining such Dirichlet and Neumann conditions is equivalent to a segmentation of the heaps. To get a fast and accurate 3D reconstruction, we propose a simple and interactive segmentation method. SfS is then applied on 0.5-meter resolution Synthetic Aperture Radar (SAR) images with more precise boundary conditions. The results show that prior segmentation is preferable to no segmentation for the stockpiles volume estimation problem. Furthermore, we show that the proposed interactive segmentation method reduces the annotation time needed for such a prior segmentation

This paper proposes a system for automatic surface volume monitoring from time series of SkySat pushframe imagery. A specific challenge of building and comparing large 3D models from SkySat data is to correct inconsistencies between the camera models associated to the multiple views that are necessary to cover the area at a given time, where these camera models are represented as Rational Polynomial Cameras (RPCs). We address the problem by proposing a date-wise RPC refinement, able to handle dynamic areas covered by sets of partially overlapping views. The cameras are refined by means of a rotation that compensates for errors due to inaccurate knowledge of the satellite attitude. The refined RPCs are then used to reconstruct multiple consistent Digital Surface Models (DSMs) from different stereo pairs at each date. RPC refinement strengthens the consistency between the DSMs of each date, which is extremely beneficial to accurately measure volumes in the 3D surface models. The system is tested in a real case scenario, to monitor large coal stockpiles. Our volume estimates are validated with measurements collected on site in the same period of time.

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks by using hyperspectral data from the satellite Sentinel-5P. By sampling Sentinel-5P spectral data at fine scale, we detect methane absorption features in the shortwave infrared wavelength range (SWIR). Our method involves two separate steps: i) background subtraction and ii) detection of local maxima in the negative logarithmic spectrum of each pixel. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections.

The evolution of the antarctic ice cap is a subject of the utmost importance for the climate science. For this reason exploiting the historic archive of SPOT 5 HRS binocular stereo imagery over the Antarctica has gained interest. However, estimating surface models of Antarctica from optical satellite imagery is a challenging task. Different factors contribute to its difficulty: the reduced contrast of the snow, the abrupt changes in elevation, and the persistent cloud cover that is often indistinguishable from the snowy ground, or is translucent, or projects shadows on the ground; all of them hinder stereo matching. For these reasons the direct application of existing satellite stereo pipelines on these images often yields unsatisfactory results. In this study we explore strategies to address these problems and improve 3D modeling on these regions. We adapt the S2P [1] pipeline and incorporate a new multiscale strategy that allows to deal with incorrect geometry estimation due to clouds or lack of texture. In addition, integrating existing very low resolution DSM (1 km per pixel) of the Antarctica allows to further filter the model to produce a cleaner 3D model.

We propose an algorithm to texture 3D models of urban regions from multiple satellite images. Our algorithm is well-suited for the case when the images are obtained from different dates, where the dynamic ranges are incompatible and the position of the shadows is different. The method relies on a robust fusion of the multiple candidate textures for each surface patch, and optionally on a geometric criterion to remove the shadows of the known objects. We showcase the results by building a 3D model of a city such that all facades are correctly textured, with uniform colors and without shadows, even if for each individual input image only one side of the buildings was visible.

The osmosis model is a parabolic equation reconstructing a composite image from an input generally given by the drift fields extracted from one or several images. This global model is sometimes a valid alternative to Poisson editing. It is particularly adapted to tasks where the input images' contrast vary wildly, as is the case for the application to shadow removal. In this paper we prove that the osmosis global parabolic equation can be advantageously be replaced by a stationary local elliptic equation. We state its existence and uniqueness result and give it a consistent numerical scheme. We stress three advantages of our numerical model: it yields fast local solvers applied on the regions of interest only. It gives a new flexibility for the boundary conditions, that can be mixed and therefore distinguish in the restoration cast shadows from shaded zones. Finally it maintains intact the target image outside its modified regions, which is not possible with the global model.

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Image stereo pairs obtained from pinhole cameras can be stereo-rectified, thus permitting to test and use the many standard stereo matching algorithms of the literature. Yet, it is well-known that pushbroom Earth observation satellites produce image pairs that are not stereo-rectifiable. Nevertheless, we show that by a new and adequate use of the satellite calibration data, one can perform a precise local stereo-rectification of large Earth images. Based on this we built a fully automatic 3D reconstruction chain for the new Pléiades Earth observation satellite. It produces 1/10 pixel accurate Earth image stereo pairs at a high resolution. Examples will be made available online to the computer vision community.

Modern Earth observation satellites are calibrated in such a way that a point on the ground can be located with an error of just a few pixels in the image domain. For many applications this error can be ignored, but this is not the case for stereo reconstruction, that requires sub-pixel accuracy. In this article we propose a method to correct this error. The method works by estimating local corrections that compensate the error relative to a reference image. The proposed method does not rely on ground control points, but only on the relative consistency of the image contents. We validate our method with Pléiades and WorldView-1 images on a representative set of geographic sites.

Using a classical result on algebraic invariants of the unimodular group, we present in this paper some basic geometric affine invariant quantities, and we use them to construct some distinctive descriptors for object detection. Although full affine invariance cannot be guaranteed due to noncommutativity of camera blur with affine maps and the domain problem (that is, the difficulty of finding an affine covariant domain), the proposed descriptors behave more robustly than SIFT with respect to affine deformations. This is supported by our comparisons both with the version of SIFT computed on an affine normalized neighborhood, and with ASIFT, which solves both the previously mentioned camera blur and domain problems by cleverly sampling the orbit of affine transformations of the images.

We present a method to compute the relative depth of moving objects in video sequences. The method relies on the fact that the boundary between two moving objects follows the movement of the object which is closest to the camera. Thus, the input of the method is a segmentation (to know the boundaries of objects) and an optical flow (to know the movement of the objects). The output of the method is a relative ordering of the neighboring segments. In fact, this output only provides a cue of the desired relative ordering, just like T-junctions typically provide a cue of the relative ordering of the objects around them. These cues can be used later as heuristics or as starting points for higher-level algorithms for image and video-processing.

We present a method for detecting appearances of logos in low-resolution video sequences. The method is based on matching of SIFT descriptors, plus several heuristics. The logos must come from a small database of possible logos. The emphasis is not on speed but on reliability, although the method can be executed in real time using a parallel computer.

The main purpose of this paper is to develop the mathematical analysis of anisotropic total variation problems with a degenerate metric and the computation of the associated Cheeger sets. We illustrate our analysis with the computation of Cheeger sets with respect to different anisotropic norms of relevance in applications to image processing. In particular, we describe the computation of global minima of geodesic active contour models, and we illustrate the use of Cheeger sets for the problem of edge linking.

We propose an edge detector based on the selection of well contrasted pieces of level lines, following the proposal of Desolneux-Moisan-Morel (DMM) [1]. The DMM edge detector has the problem of over-representation, that is, every edge is detected several times in slightly different positions. In this paper we propose two modifications of the original DMM edge detector in order to solve this problem. The first modification is a post-processing of the output using a general method to select the best representative of a bundle of curves. The second modification is the use of Canny's edge detector instead of the norm of the gradient to build the statistics. The two modifications are independent and can be applied separately. Elementary reasoning and some experiments show that the best results are obtained when both modifications are applied together.