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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)
[article]
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 classificationRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1368 - 1379[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-01071 RAB Revue Centre de documentation En réserve L003 Disponible Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery / C.C. Funk in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)
[article]
Titre : Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery Type de document : Article/Communication Auteurs : C.C. Funk, Auteur ; J. Theiler, Auteur ; C.C. Borel, Auteur ; D.A. Roberts, Auteur Année de publication : 2001 Article en page(s) : pp 1410 - 1420 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] image thermiqueRésumé : (Auteur) The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified k-means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. The authors show that clustering can reduce within-class variance and group pixels with similar correlation structures. Both of these features improve filter performance. The traditional k-means algorithm is modified to work with a sample of the image at each iteration and is tested against two hyperspectral datasets. A new “extreme” centroid initialization technique is introduced and shown to speed convergence. Several matched filtering formulations (the simple matched filter, the clutter matched filter, and the saturated matched filter) are compared for a variety of number of classes and synthetic hyperspectral images. The performance of the various clutter matched filter formulations is similar, all are about an order of magnitude better than the simple matched filter. Clustering is found to improve the performance of all matched filter formulations by a factor of two to five. Clustering in conjunction with clutter matched filtering can improve fifty-fold over the simple case, enabling very weak signals to be detected in hyperspectral images. Numéro de notice : A2001-200 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934073 En ligne : https://doi.org/10.1109/36.934073 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21894
in IEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1410 - 1420[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-01071 RAB Revue Centre de documentation En réserve L003 Disponible Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California / M. Garcia in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)
[article]
Titre : Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California Type de document : Article/Communication Auteurs : M. Garcia, Auteur ; S.L. Ustin, Auteur Année de publication : 2001 Article en page(s) : pp 1480 - 1490 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] analyse spectrale
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] littoral
[Termes IGN] pluie
[Termes IGN] savaneRésumé : (Auteur)Ecosystem responses to interannual weather variability are large and superimposed over any long-term directional climatic responses making it difficult to assign causal relationships to vegetation change. Better understanding of ecosystem responses to interannual climatic variability is crucial to predicting long-term functioning and stability. Hyperspectral data have the potential to detect ecosystem responses that are undetected by broadband sensors and can be used to scale to coarser resolution global mapping sensors, e.g., advanced very high resolution radiometer (AVHRR) and MODIS. This research focused on detecting vegetation responses to interannual climate using the airborne visible-infrared imaging spectrometer (AVIRIS) data over a natural savanna in the Central Coast Range in California. Results of linear spectral mixture analysis and assessment of the model errors were compared for two AVIRIS images acquired in spring of a dry and a wet year. The results show that mean unmixed fractions for these vegetation types were not significantly different between years due to the high spatial variability within the landscape. However, significant community differences were found between years on a pixel basis, underlying the importance of site-specific analysis. Multitemporal hyperspectral coverage is necessary to understand vegetation dynamics. Numéro de notice : A2001-206 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934079 En ligne : https://doi.org/10.1109/36.934079 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21900
in IEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1480 - 1490[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-01071 RAB Revue Centre de documentation En réserve L003 Disponible Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery / M. Lewis in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)
[article]
Titre : Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery Type de document : Article/Communication Auteurs : M. Lewis, Auteur ; V. Joosten, Auteur ; A.A. DE Gasparis, Auteur Année de publication : 2001 Article en page(s) : pp 1471 - 1479 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] airborne multispectral scanner
[Termes IGN] analyse spectrale
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] végétation
[Termes IGN] zone arideNuméro de notice : A2001-205 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934078 En ligne : https://doi.org/10.1109/36.934078 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21899
in IEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1471 - 1479[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-01071 RAB Revue Centre de documentation En réserve L003 Disponible Effect of lossy vector quantization hyperspectral data compression on retrieval of red-edge indices / S.E. Qian in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)
[article]
Titre : Effect of lossy vector quantization hyperspectral data compression on retrieval of red-edge indices Type de document : Article/Communication Auteurs : S.E. Qian, Auteur ; A.B. Hollinger, Auteur ; M. Dutkiewicz, Auteur ; et al., Auteur Année de publication : 2001 Article en page(s) : pp 1459 - 1470 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Airborne Visible/InfraRed Imaging Spectrometer
[Termes IGN] Compact airborne spectrographic imager
[Termes IGN] compression de données
[Termes IGN] image hyperspectraleNuméro de notice : A2001-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934077 En ligne : https://doi.org/10.1109/36.934077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21898
in IEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1459 - 1470[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-01071 RAB Revue Centre de documentation En réserve L003 Disponible Georegistration of airborne hyperspectral image data / C. Lee in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkHyperspectral subpixel target detection using the linear mixing model / D. Manolakis in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkL'imagerie spatiale pour la mise à jour cartographique en Afrique : un cas d'étude en Guinée-Conakry / Nicolas Baghdadi in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 163 (Juillet 2001)PermalinkInformation-theoretic assessment of sampled hyperspectral imagers / B. Aiazzi in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkA new search algorithm for feature selection in hyperspectral remote sensing images / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkRetrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks / P. Cipollini in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)Permalinkvol 39 n° 7 - July 2001 - Special issue on analysis of hyperspectral image data (Bulletin de IEEE Transactions on geoscience and remote sensing) / Geoscience and remote sensing societyPermalinkSpectral mixture analysis of simulated thermal infrared spectrometry data: an initial temperature estimate bounded TESSMA search approach / E.F. Collins in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkA spectral mixture process conditioned by Gibbs-based partitioning / R.S. Rand in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkThe impact of viewing geometry on material discriminability in hyperspectral images / P.H. Suen in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)Permalink