SUBSPACE SELECTION BASED MULTIPLE CLASSIFIER SYSTEMS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Bor-Chen Kuo, Chun-Hsiang Chuang, Cheng-Hsuan Li, Chin-Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In a typical supervised classification task, the size of training data fundamentally affects the generality of a classifier. Given a finite and fixed size of training data, the classification result may be degraded as the number of features (dimensionality) increase. Many researches have demonstrated that multiple classifier systems (MCS) or so-called ensembles can alleviate small sample size and high dimensionality concern, and obtain more outstanding and robust results than single models. One of the effective approaches for generating an ensemble of diverse base classifiers is the use of different feature subsets such as random subspace method (RSM). The objective of this research is to develop a novel ensemble technique based on cluster algorithms for strengthening RSM. The results of real data experiments show that the proposed method obtains the sound performance especially in the situation of using less number of classifiers.
Original languageEnglish
Title of host publication2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING
PublisherIEEE
Pages211
ISBN (Print)978-1-4244-4686-5
StatePublished - 2009
Event1st Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote - Grenoble, France
Duration: 26 Aug 200929 Aug 2009

Publication series

Name2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING
PublisherIEEE

Conference

Conference1st Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote
CountryFrance
City Grenoble
Period26/08/0929/08/09

Keywords

  • Hyperspectral image classification; random subspace method; kernel smoothing

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    Kuo, B-C., Chuang, C-H., Li, C-H., & Lin, C-T. (2009). SUBSPACE SELECTION BASED MULTIPLE CLASSIFIER SYSTEMS FOR HYPERSPECTRAL IMAGE CLASSIFICATION. In 2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (pp. 211). (2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING). IEEE.