標題: SUBSPACE SELECTION BASED MULTIPLE CLASSIFIER SYSTEMS FOR HYPERSPECTRAL IMAGE CLASSIFICATION
作者: Kuo, Bor-Chen
Chuang, Chun-Hsiang
Li, Cheng-Hsuan
Lin, Chin-Teng
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Hyperspectral image classification;random subspace method;kernel smoothing
公開日期: 2009
摘要: 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.
URI: http://hdl.handle.net/11536/134921
ISBN: 978-1-4244-4686-5
期刊: 2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING
起始頁: 211
結束頁: +
顯示於類別:會議論文