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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46474

Title: A Dynamic Subspace Method for Hyperspectral Image Classification
Authors: Jinn-Min Yang
Bor-Chen Kuo
Pao-Ta Yu
Chun-Hsiang Chuang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Kernel smoothing (KS);random subspace method (RSM);small sample size (SSS) classification
Date: 2010-07
Issue Date: 2018-05-21T02:55:24Z
Publisher: IEEE Transactions on Geoscience and Remote Sensing
Abstract: Abstract:
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.
Relation: 48(7), pp.2840-2853
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46474
Appears in Collections:[資訊工程學系] 期刊論文

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