11 research outputs found

    Remote Sensing for Non‐Technical Survey

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    This chapter describes the research activities of the Royal Military Academy on remote sensing applied to mine action. Remote sensing can be used to detect specific features that could lead to the suspicion of the presence, or absence, of mines. Work on the automatic detection of trenches and craters is presented here. Land cover can be extracted and is quite useful to help mine action. We present here a classification method based on Gabor filters. The relief of a region helps analysts to understand where mines could have been laid. Methods to be a digital terrain model from a digital surface model are explained. The special case of multi‐spectral classification is also addressed in this chapter. Discussion about data fusion is also given. Hyper‐spectral data are also addressed with a change detection method. Synthetic aperture radar data and its fusion with optical data have been studied. Radar interferometry and polarimetry are also addressed

    Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

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    This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

    Processing of optic and radar images.Application in satellite remote sensing of snow, ice and glaciers

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    Ce document présente une synthèse de mes activités de recherche depuis la soutenance de ma thèse en 1999. L'activité rapportée ici est celle d'un ingénieur de recherche, et donc s'est déroulée en parallèle d'une activité ``technique'' comprenant des taches d'instrumentation en laboratoire, d'instrumentation de plateformes en montagne, de raids scientifiques sur les calottes polaires, d'élaboration de projets scientifiques, d'organisation d'équipes ou d'ordre administratif. Je suis Ingénieur de recherche CNRS depuis 2004 affecté au laboratoire Gipsa-lab, une unité mixte de recherche du CNRS, de Grenoble-INP, de l'université Joseph Fourier et de l'université Stendhal. Ce laboratoire (d'environ 400 personnes), conventionné avec l'INRIA, l'Observatoire de Grenoble et l'université Pierre Mendès France, est pluridisciplinaire et développe des recherches fondamentales et finalisées sur les signaux et les systèmes complexes.}Lors de la préparation de ma thèse (mi-temps 1995-99) au LGGE, je me suis intéressé au traitement des images de microstructures de la neige, du névé et de la glace. C'est assez naturellement que j'ai rejoint le laboratoire LIS devenu Gipsa-lab pour y développer des activités de traitement des images Radar à Synthèse d'Ouverture (RSO) appliqué aux milieux naturels neige, glace et glaciers. Etant le premier à générer un interférogramme différentiel des glaciers des Alpes, j'ai continué à travailler sur la phase interférométrique pour extraire des informations de déplacement et valider ces méthodes sur le glacier d'Argentière (massif du Mont-Blanc) qui présente l'énorme avantage de se déplacer de quelques centimètres par jour. Ces activités m'ont amené à développer, en collaboration avec les laboratoires LISTIC, LTCI et IETR, des méthodes plus générales pour extraire des informations dans les images RSO.Ma formation initiale en électronique, puis de doctorat en physique m'ont amené à mettre à profit mes connaissances en traitement d'images et des signaux, en électromagnétisme, en calcul numérique, en informatique et en physique de la neige et de la glace pour étudier les problèmes de traitement des images RSO appliqués à la glace, aux glaciers et à la neige

    InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

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    Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth’s surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used

    ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

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    Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

    Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

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    Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research

    Remote Sensing of the Oceans

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    This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

    LIDAR based semi-automatic pattern recognition within an archaeological landscape

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    LIDAR-Daten bieten einen neuartigen Ansatz zur Lokalisierung und Überwachung des kulturellen Erbes in der Landschaft, insbesondere in schwierig zu erreichenden Gebieten, wie im Wald, im unwegsamen Gelände oder in sehr abgelegenen Gebieten. Die manuelle Lokalisation und Kartierung von archäologischen Informationen einer Kulturlandschaft ist in der herkömmlichen Herangehensweise eine sehr zeitaufwändige Aufgabe des Fundstellenmanagements (Cultural Heritage Management). Um die Möglichkeiten in der Erkennung und bei der Verwaltung des kulturellem Erbes zu verbessern und zu ergänzen, können computergestützte Verfahren einige neue Lösungsansätze bieten, die darüber hinaus sogar die Identifizierung von für das menschliche Auge bei visueller Sichtung nicht erkennbaren Details ermöglichen. Aus archäologischer Sicht ist die vorliegende Dissertation dadurch motiviert, dass sie LIDAR-Geländemodelle mit archäologischen Befunden durch automatisierte und semiautomatisierte Methoden zur Identifizierung weiterer archäologischer Muster zu Bodendenkmalen als digitale „LIDAR-Landschaft“ bewertet. Dabei wird auf möglichst einfache und freie verfügbare algorithmische Ansätze (Open Source) aus der Bildmustererkennung und Computer Vision zur Segmentierung und Klassifizierung der LIDAR-Landschaften zur großflächigen Erkennung archäologischer Denkmäler zurückgegriffen. Die Dissertation gibt dabei einen umfassenden Überblick über die archäologische Nutzung und das Potential von LIDAR-Daten und definiert anhand qualitativer und quantitativer Ansätze den Entwicklungsstand der semiautomatisierten Erkennung archäologischer Strukturen im Rahmen archäologischer Prospektion und Fernerkundungen. Darüber hinaus erläutert sie Best Practice-Beispiele und den einhergehenden aktuellen Forschungsstand. Und sie veranschaulicht die Qualität der Erkennung von Bodendenkmälern durch die semiautomatisierte Segmentierung und Klassifizierung visualisierter LIDAR-Daten. Letztlich identifiziert sie das Feld für weitere Anwendungen, wobei durch eigene, algorithmische Template Matching-Verfahren großflächige Untersuchungen zum kulturellen Erbe ermöglicht werden. Resümierend vergleicht sie die analoge und computergestützte Bildmustererkennung zu Bodendenkmalen, und diskutiert abschließend das weitere Potential LIDAR-basierter Mustererkennung in archäologischen Kulturlandschaften

    CIRCULARITY OF COMPLEX STOCHASTIC MODELS IN POLSAR AND MULTI-PASS INSAR IMAGES

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    Polarimetry and multi-pass interferometry extend the dimensionality of SAR data, so the necessity to have multivariate statistic (and non-Gaussian, because of the high resolution) distributions as models for these types of data: such are the SIRV (Spherically Invariant Random Vectors). However, as the statistic model becomes so complicated, correctly estimating its parameters gets difficult. More, although they are versatile, the SIRV models are not guaranteed to match the PolSAR / InSAR data. To evaluate the pertinence of those models with respect to the PolSAR data, through one of their most important statistic property, namely the circularity, it is the purpose of this paper. Index Terms — Polarimetry, SAR, circularity, SIRV 1
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