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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46496

Title: A nonparametric contextual classification based on Markov random fields
Authors: Bor-Chen Kuo
Chun-Hsiang Chuang
Chih-Sheng Huang
Chih-Cheng Hung
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Hyperspectral image classification;Bayesian contextual classification;Markov random fields
Date: 2009-08
Issue Date: 2018-05-21T06:31:08Z
Publisher: Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on
Abstract: Abstract:
In this paper a nonparametric contextual classification using both spectral and spatial information will be proposed for hyperspectral image classification. Essentially, among the classification, spatial information is acquired on the basis of Markov random field (MRF) and then joined with the nonparametric density estimation. Two MRF-based nonparametric contextual classifications based on kNN and Parzen density estimation will be introduced. We expect this combination could strengthen the capability for classifying pixels of different class labels with similar spectral values and dealing with data that has no clear numerical interpretation.
Relation: pp.1-4
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46496
Appears in Collections:[資訊工程學系] 演講及研討會

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