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Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2899 - 2912 Titre : Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Immaculada Dopido, Auteur ; Paolo Gamba, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2899 - 2912 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification dirigée [Termes IGN] image hyperspectrale Résumé : (Auteur) Classification and spectral unmixing are two important techniques for hyperspectral data exploitation. Traditionally, these techniques have been exploited independently. In this paper, we propose a new technique that exploits their complementarity. Specifically, we develop a new framework for semisupervised hyperspectral image classification that naturally integrates the information provided by discriminative classification and spectral unmixing. The idea is to assign more confidence to the information provided by discriminative classification for those pixels that can be easily catalogued due to their spectral purity. For those pixels that are more highly mixed in nature, we assign more confidence to the information provided by spectral unmixing. In this case, we use a traditional spectral unmixing chain to produce the abundance fractions of the pure signatures (endmembers) that model the mixture information at a subpixel level. The decision on which source of information is prioritized in the process is taken adaptively, when new unlabeled samples are selected and included in our semisupervised framework. In this regard, the proposed approach can adaptively integrate these two sources of information without the need to establish any weight parameters, thus exploiting the complementarity of classification and unmixing and selecting the most appropriate source of information in each case. In order to test our concept, which has similar computational complexity as traditional semisupervised classification strategies, we have used two different hyperspectral data sets with different characteristics and spatial resolution. In our experiments, we consider two different discriminative classifiers: multinomial logistic regression and probabilistic support vector machine. The obtained results indicate that the proposed approach, which jointly exploits the features provided by classification and spectral unmixing in adaptive fashion, offers an effective solution to improve- classification performance in hyperspectral scenes containing mixed pixels. Numéro de notice : A2015-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2366513 En ligne : https://doi.org/10.1109/TGRS.2014.2366513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77532 [article]

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Semisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4032 - 4044 Titre : Semisupervised self-learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Immaculada Dopido, Auteur ; Jun Li, Auteur ; Prashanth Reddy Marpu, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4032 - 4044 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image ROSIS [Termes IGN] régression logistique Résumé : (Auteur) Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the cont- xt of hyperspectral image classification. Numéro de notice : A2013-374 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2228275 En ligne : https://doi.org/10.1109/TGRS.2012.2228275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32512 [article]

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