Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 87, Issue , 2014, Pages 166-179

  Demarchi, Luca ^a   Canters, Frank ^a   Cariou, Claude ^b   Licciardi, Giorgio ^c   Chan, Jonathan Cheung Wai ^a,d  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^b UNIVERSITÉ DE RENNES 1   (France)

^c GIPSA LAB   (France)

^d RESEARCH AND INNOVATION CENTRE   (Italy)

Author keywords

Airborne high resolution hyperspectral imagery; APEX; Autoassociative Neural Network; BandClust; Data dimensionality reduction; Machine learning classifiers

Indexed keywords

ADAPTIVE BOOSTING; CLASSIFICATION (OF INFORMATION); DATA PROCESSING; DATA REDUCTION; DECISION TREES; IMAGE CLASSIFICATION; NEURAL NETWORKS; REMOTE SENSING; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

APEX; AUTOASSOCIATIVE NEURAL NETWORKS; BANDCLUST; DATA DIMENSIONALITY REDUCTION; HYPER-SPECTRAL IMAGERIES; LEARNING CLASSIFIERS;

MAPPING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; DATA SET; LAND COVER; MAPPING; PERFORMANCE ASSESSMENT; REMOTE SENSING; SENSOR; URBAN AREA;

EID: 84889672730     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2013.10.012     Document Type: Article

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