Individual Tree Species Classification from Airborne Multisensor Imagery Using Robust PCA

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 6, 2016, Pages 2554-2567

  Lee, Juheon ^a   Cai, Xiaohao ^a   Lellmann, Jan ^b   Dalponte, Michele ^a   Malhi, Yadvinder ^c   Butt, Nathalie ^c   Morecroft, Mike ^d   Schonlieb, Carola Bibiane ^a   Coomes, David A ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

^d NATURAL ENGLAND   (United Kingdom)

Author keywords

Hyperspectral imaging; image registration; image segmentation; light detection and ranging (Lidar); principal component analysis (PCA); species classification; support vector machine (SVM); Wytham Woods

Indexed keywords

BIODIVERSITY; CONSERVATION; FEATURE EXTRACTION; FORESTRY; GRAPHIC METHODS; NATURAL RESOURCES MANAGEMENT; OPTICAL RADAR; PIXELS; REMOTE SENSING; SPECTROSCOPY; STATISTICAL TESTS;

AIRBORNE REMOTE SENSING; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL SENSORS; INDIVIDUAL TREE CROWN; INDIVIDUAL TREE SPECIES CLASSIFICATIONS; LIGHT DETECTION AND RANGING; ROBUST PRINCIPAL COMPONENT ANALYSIS; SPECIES CLASSIFICATION;

PRINCIPAL COMPONENT ANALYSIS;

AIRBORNE SENSING; IMAGE CLASSIFICATION; LIDAR; PRINCIPAL COMPONENT ANALYSIS; SEGMENTATION; SUPPORT VECTOR MACHINE; TREE; WOODLAND;

ENGLAND; OXFORDSHIRE; UNITED KINGDOM; WYTHAM WOODS;

EID: 84976463532     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2569408     Document Type: Article

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