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Linear spectral random mixture analysis for hyperspectral imagery / C.I. Chang in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 375 - 392 Titre : Linear spectral random mixture analysis for hyperspectral imagery Type de document : Article/Communication Auteurs : C.I. Chang, Auteur ; S.S. Chiang, Auteur ; J.A. Smith, Auteur ; I.W. Ginsberg, Auteur Année de publication : 2002 Article en page(s) : pp 375 - 392 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] analyse spectrale [Termes IGN] classification non dirigée [Termes IGN] image hyperspectrale Résumé : (Auteur) Independent component analysis (ICA) has shown success in blind source separation and channel equalization. Its applications to remotely sensed images have been investigated in recent years. Linear spectral mixture analysis (LSMA) has been widely used for subpixel detection and mixed pixel classification. It models an image pixel as a linear mixture of materials present in an image where the material abundance fractions are assumed to be unknown and nonrandom parameters. This paper considers an application of ICA to the LSMA, referred to as ICA-based linear spectral random mixture analysis (LSRMA), which describes an image pixel as a random source resulting from a random composition of multiple spectral signatures of distinct materials in the image. It differs from the LSMA in that the abundance fractions of the material spectral signatures in the LSRMA are now considered to be unknown but random independent signal sources. Two major advantages result from the LSRMA. First, it does not require prior knowledge of the materials to be used in the linear mixture model, as required for the LSMA. Second, and most importantly, the LSRMA models the abundance fraction of each material spectral signature as an independent random signal source so that the spectral variability of materials can be described by their corresponding abundance fractions and captured more effectively in a stochastic manner. The experimental results demonstrate that the proposed LSRMA provides an effective unsupervised technique for target detection and image classification in hyperspectral imagery. Numéro de notice : A2002-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992799 En ligne : https://doi.org/10.1109/36.992799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22012 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
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065-02022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Unsupervised target detection in hyperspectral images using projection pursuit / S.S. Chiang 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 1380 - 1391 Titre : Unsupervised target detection in hyperspectral images using projection pursuit Type de document : Article/Communication Auteurs : S.S. Chiang, Auteur ; C.I. Chang, Auteur ; I.W. Ginsberg, Auteur Année de publication : 2001 Article en page(s) : pp 1380 - 1391 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détail topographique artificiel [Termes IGN] détection de cible [Termes IGN] image hyperspectrale Résumé : (Auteur) The authors present a projection pursuit (PP) approach to target detection. Unlike most of developed target detection algorithms that require statistical models such as linear mixture, the proposed PP is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest. It utilizes a projection index to explore projections of interestingness. For target detection applications in hyperspectral imagery, an interesting structure of an image scene is the one caused by man-made targets in a large unknown background. Such targets can be viewed as anomalies in an image scene due to the fact that their size is relatively small compared to their background surroundings. As a result, detecting small targets in an unknown image scene is reduced to finding the outliers of background distributions. It is known that “skewness,” is defined by normalized third moment of the sample distribution, measures the asymmetry of the distribution and “kurtosis” is defined by normalized fourth moment of the sample distribution measures the flatness of the distribution. They both are susceptible to outliers. So, using skewness and kurtosis as a base to design a projection index may be effective for target detection. In order to find an optimal projection index, an evolutionary algorithm is also developed to avoid trapping local optima. The hyperspectral image experiments show that the proposed PP method provides an effective means for target detection. Numéro de notice : A2001-198 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934071 En ligne : https://doi.org/10.1109/36.934071 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21892 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-01071	RAB	Revue	Centre de documentation	En réserve L003	Disponible