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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46502

Title: A Novel Random Subspace Method Using Spectral and Spatial Information for Hyperspectral Image Classification
Authors: Bor-Chen Kuo
Chun-Hsiang Chuang
Chih-Cheng Hung
Szu-Wei Yang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: multiple classifiers system;random subspace;hyperspectral image classification
Date: 2008-07
Issue Date: 2018-05-21T06:55:24Z
Publisher: Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Abstract: Abstract:
Many studies have demonstrated that multiple classifier systems, such as random subspace method, obtain more outstanding and robust results than a single classifier. In this study, we propose a novel RSM framework which is composed of two parts. The first part is the construction of a weighted RSM, where weights are given by two classifier-based distributions. One is the feature weighting distribution, and the other is the subspace dimensionality distribution that helps for dynamically selecting the size of subspace with respect to the employed classifiers. The second part is to introduce the spatial information estimated by the Markov random filed theory into the Bayesian classifiers used in the framework. The real data experimental results show that the proposed framework obtains satisfactory performances, and the classification maps remarkably produce fewer speckles.
Relation: pp.I-217-I-220
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46502
Appears in Collections:[資訊工程學系] 演講及研討會

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