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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)
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[article]
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 infrarougeRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 1969 - 1981[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Change detection in high-resolution land use/land cover geodatabases (at object level) / Emilio Domenech (01/04/2014)
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Documents numériques
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eurosdr_no64_a-1_1.pdfAdobe Acrobat PDFGeostatistical methods for predicting soil moisture continuously in a subalpine basin / Katherine E. Williams in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 4 (April 2014)
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Titre : Geostatistical methods for predicting soil moisture continuously in a subalpine basin Type de document : Article/Communication Auteurs : Katherine E. Williams, Auteur ; Sharolyn Anderson, Auteur Année de publication : 2014 Article en page(s) : pp 333 - 341 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Colorado (Etats-Unis)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] géostatistique
[Termes IGN] humidité du sol
[Termes IGN] hydrographie de surface
[Termes IGN] image Ikonos
[Termes IGN] pente
[Termes IGN] régression géographiquement pondéréeRésumé : (Auteur) This study presents spatial statistical methods for examining the distribution of soil moisture in a sub-alpine environment. The high local variability of soil moisture is not well characterized by spatial interpolation from dispersed data points. Interpolation using only field samples from Loch Vale, Rocky Mountain National Park, Colorado produced coarse estimates that followed mean soil moisture trends, but failed to capture local mid-slope variation. A properly specified regression model was identified by using dispersed field samples and ancillary data derived from Ikonos-2 and lidar data. This model predicted soil moisture patterns at a much finer spatial resolution. An intensive field campaign provided independent soil moisture measurements that were used to assess the model's accuracy. The modeled soil moisture estimates captured local variability associated with topographic terrain differences along mid-slope areas. Numéro de notice : A2014-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.4.333 En ligne : https://doi.org/10.14358/PERS.80.4.333 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33113
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 4 (April 2014) . - pp 333 - 341[article]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)
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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'eauRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 2029 - 2039[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 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)
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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 hyperspectraleRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 2002 - 2017[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Automated geometric correction of multispectral images from high resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B / Chabitha Devarj in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
PermalinkAutomatic registration of coastal remotely sensed imagery by affine invariant feature matching with shoreline constraint / Liang Cheng in Marine geodesy, vol 37 n° 1 (March - May 2014)
PermalinkEfficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding / Junwei Han in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
PermalinkA real-time MODIS vegetation product for land surface and numerical weather prediction models / Jonathan L. Case in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)
PermalinkSpatial and spectral image fusion using sparse matrix factorization / Bo Huang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)
PermalinkSynthetic images for evaluating topographic correction algorithms / Ion Sola in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)
PermalinkUL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification / Weiwei Sun in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
PermalinkAdaptive subpixel mapping based on a multiagent system for remote-sensing imagery / Xiong Xu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkAutomated parameterisation for multi-scale image segmentation on multiple layers / L. Drăguț in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
PermalinkA fully constrained linear spectral unmixing algorithm based on distance geometry / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
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