A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability

IEEE Transactions on Geoscience and Remote Sensing

Volumn 47, Issue 9, 2009, Pages 3180-3191

  Bruzzone, Lorenzo ^a   Persello, Claudio ^a  

^a UNIVERSITY OF TRENTO   (Italy)

Author keywords

Expectation maximization (EM) algorithm; Feature selection; Hyperspectral images; Image classification; Remote sensing; Robust features; Semisupervised feature selection; Stationary features

Indexed keywords

EXPECTATION-MAXIMIZATION (EM) ALGORITHM; FEATURE SELECTION; HYPERSPECTRAL IMAGES; ROBUST FEATURES; SEMISUPERVISED FEATURE SELECTION; STATIONARY FEATURES;

EVOLUTIONARY ALGORITHMS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; INDEPENDENT COMPONENT ANALYSIS; MAXIMUM PRINCIPLE; MULTIOBJECTIVE OPTIMIZATION; REMOTE SENSING;

SET THEORY;

ALGORITHM; ELECTROMAGNETIC FIELD; IMAGE CLASSIFICATION; OPTIMIZATION; REMOTE SENSING;

HYPERION;

EID: 70349332954     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2009.2019636     Document Type: Article

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