Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 50, Issue 1, Pages 130-148Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2011.2159613
Keywords
AdaBoost; evolutionary algorithms (EAs); genetic fuzzy-rule-based classification systems (FRBCSs) (GFRBCSs); genetic tuning; hyperspectral image classification; local feature selection; remote sensing
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This paper proposes the use of a genetic fuzzy-rulebased classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postprocessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks.
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