標題: A Dynamic Subspace Method for Hyperspectral Image Classification
作者: Yang, Jinn-Min
Kuo, Bor-Chen
Yu, Pao-Ta
Chuang, Chun-Hsiang
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Kernel smoothing (KS);random subspace method (RSM);small sample size (SSS) classification
公開日期: 1-七月-2010
摘要: Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. In this paper, we propose a novel subspace selection mechanism, named the dynamic subspace method (DSM), to improve RSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Two importance distributions are proposed to impose on the process of constructing ensemble classifiers. One is the distribution of subspace dimensionality, and the other is the distribution of band weights. Based on the two distributions, DSM becomes an automatic, dynamic, and adaptive ensemble. The real data experimental results show that the proposed DSM obtains sound performances than RSM, and that the classification maps remarkably produce fewer speckles.
URI: http://dx.doi.org/10.1109/TGRS.2010.2043533
http://hdl.handle.net/11536/5180
ISSN: 0196-2892
DOI: 10.1109/TGRS.2010.2043533
期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume: 48
Issue: 7
起始頁: 2840
結束頁: 2853
顯示於類別:期刊論文


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  1. 000281789800007.pdf