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Impact of signal contamination on the adaptive detection performance of local hyperspectral anomalies / Stefania Matteoli in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 1948 - 1968 Titre : Impact of signal contamination on the adaptive detection performance of local hyperspectral anomalies Type de document : Article/Communication Auteurs : Stefania Matteoli, Auteur ; Marco Diani, Auteur ; Giovanni Corsini, Auteur Année de publication : 2014 Article en page(s) : pp 1948 - 1968 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] contamination [Termes IGN] covariance [Termes IGN] dégradation du signal [Termes IGN] détection d'anomalie [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] rapport signal sur bruit [Termes IGN] signature spectrale [Termes IGN] variabilité Résumé : (Auteur) The effects of signal contamination of secondary data are investigated in the framework of adaptive target detection in remotely sensed hyperspectral images. In contrast to previous studies on signal contamination, the focus of this paper is the detection of targets with unknown spectral signatures (i.e., anomalies) and adaptive detection methods based on a local estimation of the background covariance matrix. Contamination due to the target signal is expected to have a more severe impact when the number of secondary data is limited. An analytical model for signal contamination is developed that allows variability in the extent of contamination. Several parameters, such as the contamination fraction of secondary data and the contaminating signal energy, are introduced, and a contaminating signal-to-interference-plus-noise ratio is derived as an objective measure of contamination. The proposed model is employed to experimentally evaluate signal contamination effects and the impact of its variability on the performance of adaptive detection of local anomalies. The outcomes of the experimental study are substantiated by validation with real hyperspectral data. The results obtained highlight the relevance that the impact of signal contamination, assessed with respect to different system parameters, may have for practical applications. This paper represents a starting point for the development of detection performance forecasting models that consider signal contamination. Numéro de notice : A2014-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2256915 En ligne : https://doi.org/10.1109/TGRS.2013.2256915 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33169 [article]

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