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Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes / J. Im in Geocarto international, vol 27 n° 5 (August 2012)  

Public [article]  inGeocarto international > vol 27 n° 5 (August 2012) . - pp 373 - 393 Titre : Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes Type de document : Article/Communication Auteurs : J. Im, Auteur ; Zhong Lu, Auteur ; J. Rhee, Auteur ; R. Jensen, Auteur Année de publication : 2012 Article en page(s) : pp 373 - 393 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme génétique [Termes IGN] classification par réseau neuronal [Termes IGN] données lidar [Termes IGN] entropie [Termes IGN] image AISA+ [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] milieu urbain [Termes IGN] New York (Etats-Unis ; état) Résumé : (Auteur) The urban landscape is dynamic and complex. As improved remote sensing data in terms of spatial and spectral characteristics became available, more sophisticated methods have been adopted for urban applications. This study proposed and evaluated a classification model incorporating feature selection, artificial immune networks and parameter optimization. Information gain, a broadly applied feature selection metric used in data mining techniques such as decision trees, was used for feature selection. Two types of information gain – binary-class entropy and multiple-class entropy – were investigated. Artificial immune networks have been recently applied to remote sensing classification and have been proven useful especially when multiple parameters of the networks are optimized through a genetic algorithm. The proposed model was tested for urban classification using hyperspectral (i.e. AISA and Hyperion) and LiDAR data over two urban study sites. Results show that the model considerably reduced processing time (70%) for classification without significant accuracy decrease. Numéro de notice : A2012-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.642898 Date de publication en ligne : 06/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.642898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31815 [article]

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