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Semisupervised learning of hyperspectral data with unknown land-cover classes / G. Jun in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 273 - 282 Titre : Semisupervised learning of hyperspectral data with unknown land-cover classes Type de document : Article/Communication Auteurs : G. Jun, Auteur ; J. Ghosh, Auteur Année de publication : 2013 Article en page(s) : pp 273 - 282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] apprentissage semi-dirigé [Termes IGN] Botswana [Termes IGN] classification bayesienne [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol [Termes IGN] régression [Termes IGN] réponse spectrale [Termes IGN] variation Résumé : (Auteur) Both supervised and semisupervised algorithms for hyperspectral data analysis typically assume that all unlabeled data belong to the same set of land-cover classes that is represented by labeled data. This is not true in general, however, since there may be new classes in the unexplored regions within an image or in areas that are geographically near but topographically distinct. This problem is more likely to occur when one attempts to build classifiers that cover wider areas; such classifiers also need to address spatial variations in acquired spectral signatures if they are to be accurate and robust. This paper presents a semisupervised spatially adaptive mixture model (SESSAMM) to identify land covers from hyperspectral images in the presence of previously unknown land-cover classes and spatial variation of spectral responses. SESSAMM uses a nonparametric Bayesian framework to apply spatially adaptive mechanisms to the mixture model with (potentially) infinitely many components. In this method, each component in the mixture has spatially adapted parameters estimated by Gaussian process regression, and spatial correlations between indicator variables are also considered. The proposed SESSAMM algorithm is applied to hyperspectral data from Botswana and from the DC Mall, where some classes are present only in the unlabeled data. SESSAMM successfully differentiates unlabeled instances of previously known classes from unknown classes and provides better results than the standard Dirichlet process mixture model and other alternatives. Numéro de notice : A2013-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2198654 En ligne : https://doi.org/10.1109/TGRS.2012.2198654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32152 [article]

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Dimensionality reduction of hyperspectral data using spectral fractal feature / K. Mukherjee in Geocarto international, vol 27 n° 6 (October 2012)  

Public [article]  inGeocarto international > vol 27 n° 6 (October 2012) . - pp 515 - 531 Titre : Dimensionality reduction of hyperspectral data using spectral fractal feature Type de document : Article/Communication Auteurs : K. Mukherjee, Auteur ; J. Ghosh, Auteur ; R. Mittal, Auteur Année de publication : 2012 Article en page(s) : pp 515 - 531 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] courbe [Termes IGN] dimension fractale [Termes IGN] image hyperspectrale [Termes IGN] réduction [Termes IGN] réponse spectrale Résumé : (Auteur) A new approach for dimensionality reduction of hyperspectral data has been proposed in this article. The method is based on extraction of fractal-based features from the hyperspectral data. The features have been generated using spectral fractal dimension of the spectral response curves (SRCs) after smoothing, interpolating and segmenting the curves. The new features so generated have then been used to classify hyperspectral data. Comparing the post classification accuracies with some other conventional dimensionality reduction methods, it has been found that the proposed method, with less computational complexity than the conventional methods, is able to provide classification accuracy statistically equivalent to those from conventional methods. Numéro de notice : A2012-512 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.642411 Date de publication en ligne : 11/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.642411 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31958 [article]

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Exploiting class hierarchies for knowledge transfer in hyperspectral data / S. Rajan in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3408 - 3417 Titre : Exploiting class hierarchies for knowledge transfer in hyperspectral data Type de document : Article/Communication Auteurs : S. Rajan, Auteur ; J. Ghosh, Auteur ; Melba M. Crawford, Auteur Année de publication : 2006 Article en page(s) : pp 3408 - 3417 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification ascendante hiérarchique [Termes IGN] classification non dirigée [Termes IGN] données multitemporelles [Termes IGN] image hyperspectrale [Termes IGN] signature spectrale Résumé : (Auteur) Obtaining ground truth for classification of remotely sensed data is time consuming and expensive, resulting in poorly represented signatures over large areas. In addition, the spectral signatures of a given class vary with location and/or time. Therefore, successful adaptation of a classifier designed from the available labeled data to classify new hyperspectral images acquired over other geographic locations or subsequent times is difficult, if minimal additional labeled data are available. In this paper, the binary hierarchical classifier is used to propose a knowledge transfer framework that leverages the information extracted from the existing labeled data to classify spatially separate and multitemporal test data. Experimental results show that in the absence of any labeled data in the new area, the approach is better than a direct application of the original classifier on the new data. Moreover, when small amounts of the labeled data are available from the new area, the framework offers further improvements through semisupervised learning mechanisms and compares favorably with previously proposed methods. Copyright IEEE Numéro de notice : A2006-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.878442 En ligne : https://doi.org/10.1109/TGRS.2006.878442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28251 [article]

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Best-bases feature extraction algorithms for classification of hyperspectral data / Satish Kumar in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1368 - 1379 Titre : Best-bases feature extraction algorithms for classification of hyperspectral data Type de document : Article/Communication Auteurs : Satish Kumar, Auteur ; J. Ghosh, Auteur ; Melba M. Crawford, Auteur Année de publication : 2001 Article en page(s) : pp 1368 - 1379 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) Due to advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in hundreds of bands. Algorithms that both reduce the dimensionality of the data sets and handle highly correlated bands are required to exploit the information in these data sets effectively. the authors propose a set of best-bases feature extraction algorithms that are simple, fast, and highly effective for classification of hyperspectral data. These techniques intelligently combine subsets of adjacent bands into a smaller number of features. Both top-down and bottom-up algorithms are proposed. The top-down algorithm recursively partitions the bands into two (not necessarily equal) sets of bands and then replaces each final set of bands by its mean value. The bottom-up algorithm builds an agglomerative tree by merging highly correlated adjacent bands and projecting them onto their Fisher direction, yielding high discrimination among classes. Both these algorithms are used in a pairwise classifier framework where the original C-class problem is divided into a set of (2C) two-class problems. The new algorithms (1) find variable length bases localized in wavelength, (2) favor grouping highly correlated adjacent bands that, when merged either by taking their mean or Fisher linear projection, yield maximum discrimination, and (3) seek orthogonal bases for each of the (2C) two-class problems into which a C-class problem can be decomposed. Experiments on an AVIRIS data set for a 12-class problem show significant improvements in classification accuracies while using a much smaller number of features Numéro de notice : A2001-197 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934070 En ligne : https://ieeexplore.ieee.org/document/934070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21891 [article]

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