Robust Control of Varying Weak Hyperspectral Target Detection With Sparse Nonnegative Representation
| Autor: | Olivier Michel, Florent Chatelain, Raphael Bacher, Celine Meillier |
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| Přispěvatelé: | Centre de Recherche Astrophysique de Lyon (CRAL), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-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 Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-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 Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique (GIPSA-SAIGA), Département Automatique (GIPSA-DA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-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 Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Département Images et Signal (GIPSA-DIS), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), European Project: 339659,EC:FP7:ERC,ERC-2013-ADG,MUSICOS(2014), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science detection Context (language use) 02 engineering and technology 01 natural sciences FDR Methodology (stat.ME) Signal-to-noise ratio 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Test statistic Electrical and Electronic Engineering 010303 astronomy & astrophysics Spectrograph Statistics - Methodology global control Pixel business.industry Detector Hyperspectral imaging 020206 networking & telecommunications Pattern recognition Signal Processing Artificial intelligence Robust control business [STAT.ME]Statistics [stat]/Methodology [stat.ME] |
| Zdroj: | IEEE Transactions on Signal Processing IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2017, 65 (13), pp.3538-3550. ⟨10.1109/TSP.2017.2688965⟩ IEEE Transactions on Signal Processing, 2017, 65 (13), pp.3538-3550. ⟨10.1109/TSP.2017.2688965⟩ |
| ISSN: | 1941-0476 1053-587X |
| Popis: | International audience; In this study, a multiple-comparison approach is developed for detecting faint hyperspectral sources. The detection method relies on a sparse and non-negative representation on a highly coherent dictionary to track a spatially varying source. A robust control of the detection errors is ensured by learning the test statistic distributions on the data. The resulting control is based on the false discovery rate, to take into account the large number of pixels to be tested. This method is applied to data recently recorded by the three-dimensional spectrograph Multi-Unit Spectrograph Explorer (MUSE). |
| Databáze: | OpenAIRE |
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