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4 results on '"REJECT CLASS"'

1. Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

Author

Alexandre Alakian and Véronique Achard

Subjects
classification, hyperspectral, reflectance, reject class, specific absorption, spectral index, Science
Abstract

A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile, asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

Published
2020
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2. Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

Author

Alakian, Alexandre, Achard, Véronique, DTIS, ONERA, Université Paris Saclay [Palaiseau], ONERA-Université Paris-Saclay, ONERA / DOTA, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, and GREC, christine

Subjects
INDICE, [SPI] Engineering Sciences [physics], TRAINING, Science, SPECTRAL INDEX, [MATH] Mathematics [math], [INFO] Computer Science [cs], CLASSIFICATION, [PHYS] Physics [physics], APPRENTISSAGE, [SPI]Engineering Sciences [physics], REJECT CLASS, HYPERSPECTRAL, CRITÈRES SPECTRAUX, [INFO]Computer Science [cs], TRANSFER, [MATH]Mathematics [math], CLASSE DE REJET, [PHYS]Physics [physics], NON SUPERVISÉ, STATISTIQUE, TRANSFERT, RÉFLECTANCE, REFLECTANCE, PHYSIQUE, SPECIFIC ABSORPTION, VÉGÉTATION, DÉTECTION, UNSUPERVISED, ABSORPTION SPÉCIFIQUE
Abstract

A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile , asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400&ndash, 2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type, (ii) does not require training, criteria are pre-defined, (iii) includes a reject class, this class reduces misclassifications, (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

Published
2020

3. Sound classification in indoor environment thanks to belief functions

Author

Labourey, Quentin, Pellerin, Denis, Rombaut, Michèle, Aycard, Olivier, Garbay, Catherine, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), 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), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)

Subjects
Indoor sounds, Index Terms— Sound classification, Features selection, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Reject class, Belief functions
Abstract

International audience; Sounds provide substantial information on human activities in an indoor environment, such as an apartment or a house, but it is a difficult task to classify them, mainly due to the variability and the diversity of realization of sounds in those environments. In this paper, sounds are considered as a class of information, to be mixed with other modalities (video in particular) in the design of ambient monitoring systems. As a consequence, we propose a classification scheme aimed at (i) exploiting the specificities of this modality with respect to others and (ii) leaving doubtful events for further analysis, so that the risk of errors is overall minimized. A dedicated taxonomy together with belief functions are proposed in this respect. Belief functions are an adapted way to face the variability of sounds, as they are able to quantify their impossibility to classify the signals when it differs too much from what is known by creating class of doubt. The algorithm is tested on a dataset composed of real-life signals.

Published
2015

4. Sound classification in indoor environment thanks to belief functions

Author

Quentin Labourey, Michèle Rombaut, Catherine Garbay, Olivier Aycard, Denis Pellerin, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), 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), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)

Subjects
Indoor sounds, Index Terms— Sound classification, Class (computer programming), Modality (human–computer interaction), Computer science, Reject class, Speech recognition, Feature extraction, Realization (linguistics), Task (project management), Features selection, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Taxonomy (general), Face (geometry), Belief functions
Abstract

International audience; Sounds provide substantial information on human activities in an indoor environment, such as an apartment or a house, but it is a difficult task to classify them, mainly due to the variability and the diversity of realization of sounds in those environments. In this paper, sounds are considered as a class of information, to be mixed with other modalities (video in particular) in the design of ambient monitoring systems. As a consequence, we propose a classification scheme aimed at (i) exploiting the specificities of this modality with respect to others and (ii) leaving doubtful events for further analysis, so that the risk of errors is overall minimized. A dedicated taxonomy together with belief functions are proposed in this respect. Belief functions are an adapted way to face the variability of sounds, as they are able to quantify their impossibility to classify the signals when it differs too much from what is known by creating class of doubt. The algorithm is tested on a dataset composed of real-life signals.

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