NDLI: A nonparametric contextual classification based on Markov random fields

Content Provider	IEEE Xplore Digital Library
Author	Bor-Chen Kuo Chun-Hsiang Chuang Chih-Sheng Huang Chih-Cheng Hung
Copyright Year	2009
Description	Author affiliation: Hendri Purnawan Southern Polytechnic State University Marietta, GA 30067, USA (Chih-Cheng Hung) \|\| Graduate School of Educational Measurement and Statistics, National Taichung University, Taiwan (Bor-Chen Kuo; Chun-Hsiang Chuang; Chih-Sheng Huang)
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.
Starting Page	1
Ending Page	4
File Size	741205
Page Count	4
File Format	PDF
ISBN	9781424446865
DOI	10.1109/WHISPERS.2009.5288978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-26
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Density measurement Bayesian methods Bayesian contextual classification Feature extraction Kernel Statistics State estimation Pixel Markov random fields Hyperspectral imaging Image classification Hyperspectral image classification
Content Type	Text
Resource Type	Article

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