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11 results on '"Lorenzo Bruzzone"'

1. Direct Observations of Asteroid Interior and Regolith Structure: Science Measurement Requirements

Author

Jan Thimo Grundmann, Jens Biele, Stephan Ulamec, B. Grieger, Oriane Gassot, Yasumasa Kasaba, Antonella Barucci, Dirk Plettemeier, Erik Asphaug, Petr Schaffer, Valérie Ciarletti, Marco Delbo, Julie Bellerose, Wenzhe Fa, Jérémie Lasue, Sonia Zine, Ian Carnelli, Alain Herique, Roberto Orosei, Patrick Michel, Pierre Beck, Michael Küppers, Wlodek Kofman, J. Du, J. Gil Fernandez, R. RodriguezSuquet, Jerzy Grygorczuk, B. Agnus, Martin Laabs, R. Granados-Alfaro, J. C. Souyris, Christian Krause, Christelle Eyraud, Naomi Murdoch, Özgür Karatekin, Marta Tokarz, C. Buck, Anny Chantal Levasseur-Regourd, Essam Heggy, P. W. Bousquet, X. Du, Juergen Oberst, Colin Snodgrass, Caroline Lange, Stefano Mottola, Marco Mutze, A. Cheng, Lorenzo Bruzzone, Atsushi Kumamoto, Yves Rogez, S. Rochat, Simon F. Green, Alfred Mallet, T. Kobayashi, Ronny Hahnel, Jan-Erik Wahlund, Tra-Mi Ho, Lydie Bonal, Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ASU School of Earth and Space Exploration (SESE), Arizona State University [Tempe] (ASU), Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA (UMR_8109)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Deutsches Zentrum für Luft- und Raumfahrt (DLR), Centre National d'Études Spatiales [Toulouse] (CNES), University of Trento [Trento], European Space Research and Technology Centre (ESTEC), Agence Spatiale Européenne = European Space Agency (ESA), Johns Hopkins University Applied Physics Laboratory [Laurel, MD] (APL), PLANETO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Joseph Louis LAGRANGE (LAGRANGE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Peking University [Beijing], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), HIPE (HIPE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Planetary and Space Sciences [Milton Keynes] (PSS), School of Physical Sciences [Milton Keynes], Faculty of Science, Technology, Engineering and Mathematics [Milton Keynes], The Open University [Milton Keynes] (OU)-The Open University [Milton Keynes] (OU)-Faculty of Science, Technology, Engineering and Mathematics [Milton Keynes], The Open University [Milton Keynes] (OU)-The Open University [Milton Keynes] (OU), European Space Astronomy Centre (ESAC), DLR Institut für Planetenforschung, Deutsches Zentrum für Luft- und Raumfahrt [Berlin] (DLR), ASTRONIKA Sp. Zo.o., USC Viterbi School of Engineering, University of Southern California (USC), Royal Observatory of Belgium [Brussels] (ROB), Tohoku University [Sendai], Korea Institute of Geoscience and Mineral Resources, Space Research Centre of Polish Academy of Sciences (CBK), Polska Akademia Nauk = Polish Academy of Sciences (PAN), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Technical University of Berlin / Technische Universität Berlin (TU), Istituto di Radioastronomia [Bologna] (IRA), Istituto Nazionale di Astrofisica (INAF), Institut für Nachrichtentechnik [Dresden] (IfN), Swedish Institute of Space Physics [Uppsala] (IRF), Centre National d'Études Spatiales [Toulouse] (CNES)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), European Space Agency (ESA), Agence Spatiale Européenne (ESA), IMPEC - LATMOS, Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dresden (TUD), Royal Observatory of Belgium [Brussels], Polska Akademia Nauk (PAN), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Technische Universität Berlin (TUB), Istituto di Radioastronomia (IRA), The Open University [Milton Keynes] (OU), California Institute of Technology (CALTECH)-NASA, Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), ITA, USA, GBR, FRA, DEU, BEL, KOR, JPN, NLD, POL, CHN, SWE, Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), and Technische Universität Berlin (TU)

Subjects
Radar tomography, Atmospheric Science, Solar System, 010504 meteorology & atmospheric sciences, [SDU.ASTR.EP]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Earth and Planetary Astrophysics [astro-ph.EP], Aerospace Engineering, 01 natural sciences, Astrobiology, law.invention, Gravitational field, law, Asteroid deep internal structure, Asteroid regolith, Dielectric properties, Near-earth asteroid, Space and Planetary Science, Near-Earth Asteroid, 0103 physical sciences, Radar, 010303 astronomy & astrophysics, 0105 earth and related environmental sciences, Near-Earth object, [SCCO.NEUR]Cognitive science/Neuroscience, Neurosciences, Astronomy and Astrophysics, Regolith, Accretion (astrophysics), Depth sounding, Geophysics, Asteroid, Radar Tomography, General Earth and Planetary Sciences, Geology
Abstract

International audience; Our knowledge of the internal structure of asteroids is, so far, indirect – relying entirely on inferences from remote sensing observations of the surface, and theoretical modeling of formation and evolution. What are the bulk properties of the regolith and deep interior? And what are the physical processes that shape asteroid internal structures? Is the composition and size distribution observed on the surface representative of the bulk? These questions are crucial to understand small bodies’ history from accretion in the early Solar System to the present, and direct measurements are needed to answer these questions for the benefit of science as well as for planetary defense or exploration.Radar is one of the main instruments capable of sounding asteroids to characterize internal structure from sub-meter to global scale. In this paper, we review the science case for direct observation of the deep internal structure and regolith of a rocky asteroid of kilometer size or smaller. We establish the requirements and model dielectric properties of asteroids to outline a possible instrument suite, and highlight the capabilities of radar instrumentation to achieve these observations. We then review the expected science return including secondary objectives contributing to the determination of the gravitational field, the shape model, and the dynamical state. This work is largely inherited from MarcoPolo-R and AIDA/AIM studies.

Published
2018

2. Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images

Author

Mauro Dalla Mura, Gabriele Cavallaro, Lorenzo Bruzzone, Jon Atli Benediktsson, University of Iceland [Reykjavik], GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Department of Information Engineering and Computer Science (DISI), and University of Trento [Trento]

Subjects
extended attribute profiles, business.industry, Computer science, Feature extraction, Hyperspectral imaging, Pattern recognition, self-dual attribute profiles, Geotechnical Engineering and Engineering Geology, Random forest, Dual (category theory), Support vector machine, Set (abstract data type), remote sensing, attribute profiles, mathematical morphology, Artificial intelligence, Index Terms—Attribute filters, non-parametric weighted feature extraction, Electrical and Electronic Engineering, business, Spatial analysis, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; —In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by non-parametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines (SVMs) and Random Forest (RF) are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended attribute profiles (EAPs).

Published
2015

3. Change detection in vhr images based on morphological attribute profiles

Author

Nicola Falco, Francesca Bovolo, Mauro Dalla Mura, Lorenzo Bruzzone, Jon Atli Benediktsson, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Department of Information Engineering and Computer Science (DISI), University of Trento [Trento], and University of Iceland [Reykjavik]

Subjects
Image fusion, 010504 meteorology & atmospheric sciences, Basis (linear algebra), Computer science, business.industry, Feature extraction, 0211 other engineering and technologies, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, Geotechnical Engineering and Engineering Geology, 01 natural sciences, Panchromatic film, Transformation (function), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, Image sensor, business, Image resolution, Change detection, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

International audience; A new approach to change detection in very high resolution remote sensing images based on morphological attribute profiles (APs) is presented. A multiresolution contextual transformation performed by APs allows the extraction of geometrical features related to the structures within the scene at different scales. The temporal changes are detected by comparing the geometrical features extracted from the image of each date. The experiments performed on panchromatic QuickBird images related to an urban area show the effectiveness of the proposed technique in detecting changes on the basis of the spatial morphology by preserving geometrical detail.

Published
2013

4. Natural radio emission of Jupiter as interferences for radar investigations of the icy satellites of Jupiter

Author

Baptiste Cecconi, Donald D. Blankenship, Lorenzo Bruzzone, Alain Herique, Daniel Santos-Costa, Wlodek Kofman, Jean-Louis Bougeret, Giovanni Alberti, Sebastien Hess, M. R. Santovito, Ph. Zarka, Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Helmholtz zentrum für Schwerionenforschung GmbH (GSI), UJF-Grenoble I/CNRS, Université Joseph Fourier - Grenoble 1 (UJF), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG ), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Faculty of Environment and Natural Resources, University of Freiburg [Freiburg], University of Trento [Trento], Ingénieurs, Techniciens et Administratifs, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, and Physique des plasmas

Subjects
010504 meteorology & atmospheric sciences, Astrophysics::High Energy Astrophysical Phenomena, Astrophysics::Cosmology and Extragalactic Astrophysics, 7. Clean energy, 01 natural sciences, Radio spectrum, Jovian, law.invention, Radio telescope, Jupiter, symbols.namesake, Exploration of Jupiter, law, 0103 physical sciences, Radar, 010303 astronomy & astrophysics, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Remote sensing, Physics, [PHYS]Physics [physics], Europa Orbiter, Astronomy, Astronomy and Astrophysics, Galilean moons, 13. Climate action, Space and Planetary Science, Physics::Space Physics, symbols, Astrophysics::Earth and Planetary Astrophysics, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract

Radar instruments are part of the core payload of the two Europa Jupiter System Mission (EJSM) spacecraft: NASA-led Jupiter Europa Orbiter (JEO) and ESA-led Jupiter Ganymede Orbiter (JGO). At this point of the project, several frequency bands are under study for radar, which ranges between 5 and 50 MHz. Part of this frequency range overlaps with that of the natural jovian radio emissions, which are very intense in the decametric range, below 40 MHz. Radio observations above 40 MHz are free of interferences, whereas below this threshold, careful observation strategies have to be investigated. We present a review of spectral intensity, variability and sources of these radio emissions. As the radio emissions are strongly beamed, it is possible to model the visibility of the radio emissions, as seen from the vicinity of Europa or Ganymede. We have investigated Io-related radio emissions as well as radio emissions related to the auroral oval. We also review the radiation belts synchrotron emission characteristics. We present radio sources visibility products (dynamic spectra and radio source location maps, on still frames or movies), which can be used for operation planning. This study clearly shows that a deep understanding of the natural radio emissions at Jupiter is necessary to prepare the future EJSM radar instrumentation. We show that this radio noise has to be taken into account very early in the observation planning and strategies for both JGO and JEO. We also point out possible synergies with RPW (Radio and Plasma Waves) instrumentations.

Published
2012

5. Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis

Author

Lorenzo Bruzzone, Jocelyn Chanussot, Jon Atli Benediktsson, A. Villa, Mauro Dalla Mura, University of Iceland [Reykjavik], Department of Information Engineering and Computer Science (DISI), University of Trento [Trento], Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Computer science, Feature extraction, 0211 other engineering and technologies, 02 engineering and technology, Mathematical morphology, 01 natural sciences, remote sensing, mathematical morphology, Electrical and Electronic Engineering, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Contextual image classification, business.industry, extended attribute profile (EAP), Hyperspectral imaging, Pattern recognition, decision fusion, Geotechnical Engineering and Engineering Geology, Independent component analysis, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Attribute filters, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Principal component analysis, Artificial intelligence, business, independent component analysis (ICA), Subspace topology
Abstract

International audience; In this letter, a technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images. The ICA maps the data into a subspace in which the components are as independent as possible. APs, which are extracted by using several attributes, are applied to each image associated with an extracted independent component, leading to a set of extended EAPs. Two approaches are presented for including the computed profiles in the analysis. The features extracted by the morphological processing are then classified with an SVM. The experiments carried out on two hyperspectral images proved the effectiveness of the proposed technique.

Published
2011

6. Introduction to the Issue on Advances in Remote Sensing Image Processing

Author

Gustau Camps-Valls, Jocelyn Chanussot, Lorenzo Bruzzone, Jon Atli Benediktsson, Image Processing Laboratory (IPL), Universitat de València (UV), University of Iceland [Reykjavik], Department of Information Engineering and Computer Science (DISI), University of Trento [Trento], GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Image fusion, 010504 meteorology & atmospheric sciences, Remote sensing application, business.industry, Computer science, Real-time computing, 0211 other engineering and technologies, Remote sensing image processing, Automatic processing, Image processing, 02 engineering and technology, 01 natural sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Signal Processing, Digital image processing, Snapshot (computer storage), Computer vision, Artificial intelligence, Electrical and Electronic Engineering, business, Image resolution, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

The papers in this special issue span a wide range of problems arising in modern remote sensing data analysis and provide a snapshot in the state-of-the-art of remote sensing image processing. More advances are expected in the near future, mainly due to the increasing user demands in terms of spatial, spectral, and temporal resolutions of data, and of products generated from these data by automatic processing techniques.

Published
2011

7. Classification of hyperspectral images by using morphological attribute filters and independent component analysis

Author

A. Villa, Lorenzo Bruzzone, Jon Atli Benediktsson, Jocelyn Chanussot, Mauro Dalla Mura, Chanussot, Jocelyn, University of Iceland [Reykjavik], Department of Information Engineering and Computer Science (DISI), University of Trento [Trento], Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, Feature extraction, 0211 other engineering and technologies, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Mathematical morphology, remote sensing, 0202 electrical engineering, electronic engineering, information engineering, attribute filters, Computer vision, Image resolution, 021101 geological & geomatics engineering, Pixel, Contextual image classification, business.industry, Hyperspectral imaging, Pattern recognition, decision fusion, 16. Peace & justice, Independent component analysis, ComputingMethodologies_PATTERNRECOGNITION, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], independent component analysis, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Principal component analysis, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

International audience; In this paper, a technique based on Independent Component Analysis (ICA) and morphological attribute filters is presented for the classification of high geometrical resolution hyperspectral images. The ICA is computed instead of the conventional principal component analysis (PCA) in order to better model the information in the hyperspectral image. The spatial characteristics of the objects in the scene are modeled by different multi-level attribute filters. Moreover, a method for increasing the robustness of the analysis based on a decision fusion strategy is proposed. A hyperspectral high resolution image acquired over the city of Pavia (Italy) was considered in the experiments.

Published
2010

8. Recent Advances in Techniques for Hyperspectral Image Processing

Author

J. Brazile, Anthony J. Gualtieri, James C. Tilton, Mattia Marconcini, Paolo Gamba, G. Trianni, Jocelyn Chanussot, Jon Atli Benediktsson, Lorenzo Bruzzone, Antonio Plaza, Mathieu Fauvel, Joseph W. Boardman, Gustavo Camps-Valls, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and University of Iceland [Reykjavik]

Subjects
010504 meteorology & atmospheric sciences, Computer science, 0211 other engineering and technologies, Soil Science, Image processing, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Computer vision, Computers in Earth Sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Data processing, Contextual image classification, business.industry, Hyperspectral imaging, Geology, Imaging spectroscopy, Information extraction, Kernel method, Snapshot (computer storage), Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific appli- cations, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of- the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms.

Published
2007

9. Advanced Processing of Hyperspectral Images

Author

James C. Tilton, Joseph W. Boardman, G. Trianni, Gustau Camps-Valls, Jocelyn Chanussot, Jon Atli Benediktsson, Antonio Plaza, Paolo Gamba, Anthony J. Gualtieri, Lorenzo Bruzzone, J. Brazile, Mathieu Fauvel, Laboratoire des images et des signaux (LIS), and Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Basis (linear algebra), business.industry, Computer science, 0211 other engineering and technologies, Hyperspectral imaging, 020206 networking & telecommunications, Image processing, 02 engineering and technology, computer.software_genre, Reflectivity, Full spectral imaging, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Artificial intelligence, Data mining, business, Focus (optics), computer, 021101 geological & geomatics engineering, Curse of dimensionality
Abstract

International audience; Hyperspectral imaging offers the possibility of characterizing materials and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar energy with the molecular structure of the material. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral data processing. Our main focus is on the development of approaches able to naturally integrate the spatial and spectral information available from the data. Special attention is paid to techniques that circumvent the curse of dimensionality introduced by high-dimensional data spaces. Experimental results, focused in this work on a specific case-study of urban data analysis, demonstrate the success of the considered techniques. This paper represents a first step towards the development of a quantitative and comparative assessment of advances in hyperspectral data processing techniques.

Published
2006

10. Soil surface roughness modeling: limit of global characterization in remote sensing

Author

O. Taconet, Olivier Chimi-Chiadjeu, Edwige Vannier, Richard Dusséaux, ESTER - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Lorenzo Bruzzone, SPACE - LATMOS, and Cardon, Catherine

Subjects
Surface (mathematics), 010504 meteorology & atmospheric sciences, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Gaussian, Surface finish, 01 natural sciences, symbols.namesake, Surface roughness, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Digital elevation model, 0105 earth and related environmental sciences, Remote sensing, Autocorrelation, Statistical parameter, Modeling, Agriculture, 04 agricultural and veterinary sciences, Natural surfaces, 15. Life on land, Geography, Photogrammetry, Linear filtering, 040103 agronomy & agriculture, symbols, 0401 agriculture, forestry, and fisheries, [SDU.STU.GP] Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms
Abstract

International audience; Many scientists use a global characterization of bare soil surface random roughness. Surface roughness is often characterized by statistical parameters deduced from its autocorrelation function. Assuming an autocorrelation model and a Gaussian height distribution, some authors have developed algorithms for numerical generation of soil surfaces that have the same statistical properties. This approach is widespread and does not take into account morphological aspects of the soil surface micro-topography. Now a detail surface roughness analysis reveals that the micro-topography is structured by holes, aggregates and clods. In the present study, we clearly show that when describing surface roughness as a whole, some information related to morphological aspects is lost. Two Digital Elevation Model (DEM) of a same natural seedbed surface were recorded by stereo photogrammetry. After estimating global parameters of these natural surfaces, we generated numerical surfaces of the same average characteristics by linear filtering. Big aggregates and clods were then captured by a contour-based approach. We show that the two-dimensional autocorrelation functions of generated surfaces and of the two agricultural surfaces are close together. Nevertheless, the number and shape of segmented object contours change from generated surfaces to the natural surfaces. Generated surfaces show fewer and bigger segmented objects than in the natural case. Moreover, the shape of some segmented objects is unrealistic in comparison to real clods, which have to be convex and of low circularity.

Published
2013

11. Machine learning techniques in remote sensing data analysis

Author

Jon Atli Benediktsson, Björn Waske, Jocelyn Chanussot, Mathieu Fauvel, University of Iceland [Reykjavik], Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Gustavo Camps-Valls, Lorenzo Bruzzone, Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, 0211 other engineering and technologies, 02 engineering and technology, Machine learning, computer.software_genre, Field (computer science), neural networks (NN) in pattern recognition and remote sensing context, development in field of (supervised) classification machine learning concepts, machine learning techniques in remote sensing data analysis, 0202 electrical engineering, electronic engineering, information engineering, Instance-based learning, remote sensing paradigms, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, Remote sensing, feature extraction and feature selection and dimensionality reduction, Artificial neural network, business.industry, machine learning (ML), Multiple classifier, machine learning algorithms in remote sensing and supervised classification, Support vector machine, remote sensing challenges, Remote sensing (archaeology), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], ISODATA (iterative self-organizing data analysis), Unsupervised learning, artificial intelligence area and learning from data, 020201 artificial intelligence & image processing, Artificial intelligence, Data mining, Hyper-heuristic, business, computer, Tasseled Cap Transformation
Abstract

Several applications have been developed in the field of remote sensing image analysis during the last decades. Besides well-known statistical approaches, many recent methods are based on techniques taken from the field of machine learning. A major aim of machine learning algorithms in remote sensing is supervised classification, which is perhaps the most widely usedimageclassificationapproach.Inthischapterabriefintroductiontomachinelearningand the different paradigms in remote sensing is given. Moreover this chapter briefly discusses the use of recent developments in supervised classification techniques such as neural networks, support vector machines and multiple classifier systems.

Published
2009

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