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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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065-2014041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Bayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto 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 1969 - 1981 Titre : Bayesian context-dependent learning for anomaly classification in hyperspectral imagery Type de document : Article/Communication Auteurs : Christopher Ratto, Auteur ; Kenneth D. Morton, Auteur ; Leslie M. Collins, Auteur ; Peter A. Torrione, Auteur Année de publication : 2014 Article en page(s) : pp 1969 - 1981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification contextuelle [Termes IGN] détection d'objet [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste [Termes IGN] rayonnement infrarouge Résumé : (Auteur) Many remote sensing applications involve the classification of anomalous responses as either objects of interest or clutter. This paper addresses the problem of anomaly classification in hyperspectral imagery (HSI) and focuses on robustly detecting disturbed earth in the long-wave infrared (LWIR) spectrum. Although disturbed earth yields a distinct LWIR signature that distinguishes it from the background, its distribution relative to clutter may vary over different environmental contexts. In this paper, a generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery. The proposed framework combines sparse classification models with either supervised or discriminative context identification to pool information across contexts and improve classification overall. Experiments are performed with data from a LWIR landmine detection system. Contexts are learned from endmember abundances extracted from the background near each detected anomaly. Classification performance is compared with single-classifier approaches using the same information as well as other baseline anomaly detectors from the literature. Results indicate that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain. Numéro de notice : A2014-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257175 En ligne : https://doi.org/10.1109/TGRS.2013.2257175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33170 [article]

Exemplaires(1)

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065-2014041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Progressive band selection of spectral unmixing for hyperspectral imagery / Chein-I Chang 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 2002 - 2017 Titre : Progressive band selection of spectral unmixing for hyperspectral imagery Type de document : Article/Communication Auteurs : Chein-I Chang, Auteur ; Keng-Hao Liu, Auteur Année de publication : 2014 Article en page(s) : pp 2002 - 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] image hyperspectrale Résumé : (Auteur) A new band selection (BS), called progressive BS (PBS) of spectral unmixing for hyperspectral imagery is being presented. It is quite different from the traditional BS in the sense that the former adapts the number of selected bands, p to various endmembers used for spectral unmixing, while the latter fixes the value of p at a constant for all endmembers. Due to the fact that different endmembers post various levels of difficulty in discrimination, each endmember should have its own custom-selected bands to specify its spectral characteristics. In order to address this issue, p is composed of two values, one value determined by virtual dimensionality to accommodate each of endmembers and the other is determined by a new concept of band dimensionality allocation to account for discrminability among endmembers. In order to find appropriate bands to be used for PBS, band prioritization and band de-correlation are included to rank bands according to significance of band information and to remove interband redundancy, respectively. As a result, spectral unmixing can be performed progressively by selecting different bands for various endmembers, a task that the traditional BS cannot accomplish. The effectiveness and advantages of using PBS over BS are also demonstrated by experiments. Numéro de notice : A2014-268 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257604 En ligne : https://doi.org/10.1109/TGRS.2013.2257604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33171 [article]

Exemplaires(1)

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065-2014041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Hyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation / Allan W. Yarbrough 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 2029 - 2039 Titre : Hyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation Type de document : Article/Communication Auteurs : Allan W. Yarbrough, Auteur ; Michael J. Mendenhall, Auteur ; Richard K. Martin, Auteur ; Steven T. Fiorino, Auteur Année de publication : 2014 Article en page(s) : pp 2029 - 2039 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'erreur [Termes IGN] données météorologiques [Termes IGN] erreur de classification [Termes IGN] estimation statistique [Termes IGN] filtrage numérique d'image [Termes IGN] filtre spectral [Termes IGN] humidité de l'air [Termes IGN] image hyperspectrale [Termes IGN] transfert radiatif [Termes IGN] vapeur d'eau Résumé : (Auteur) Accurate target detection and classification in hyperspectral imagery require that the spectral measurements by the imager match as closely as possible the known “true” target as collected under controlled conditions and stored in a target database. Therefore, the effect of the radiation source and the atmosphere must be factored out of the result before detection is attempted. Our objective is to evaluate detection error due to the error in estimating the atmospherics. We apply a range of atmospheric water vapor profiles, corresponding to different relative humidities, to a model-based prediction of the radiative transfer to examine the effect of water vapor on simulated hyperspectral imagery. These profiles are taken from known distribution percentiles as obtained from historic meteorological measurements close to the sites being simulated. We quantify the expected detection error for the adaptive matched filter, as measured by the receiver operating characteristic (ROC) and the area under the ROC curve, given the range of atmospheric conditions in the historic profile. We discover that, depending on the target, and given the uncertainty as to the true atmospheric conditions, detection rates improve on average across the historic range when we assume the atmospheric profile is at the 35th percentile of atmospheric relative humidity instead of the 50th percentile. Numéro de notice : A2014-269 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257797 En ligne : https://doi.org/10.1109/TGRS.2013.2257797 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33172 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014041	RAB	Revue	Centre de documentation	En réserve L003	Disponible