Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery

Li, Shanshan; Zhang, Bing; Chen, Dongmei; Gao, Lianru; Peng, Man

doi:doi:10.1117/1.3609847

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Journal of Applied Remote Sensing / Volume 5 / Research Papers

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Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery

J. Appl. Remote Sens. 5, 053538 (Jul 15, 2011); http://dx.doi.org/10.1117/1.3609847

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Abstract

References (24)

Shanshan Li, Bing Zhang, and Lianru Gao

Chinese Academy of Sciences, Center for Earth Observation and Digital Earth, 100094 Beijing, China

Dongmei Chen

Queen's University, Department of Geography, K7L3N6 Kingston, Ontario, Canada

Man Peng

Institute of Remote Sensing Application, Chinese Academy of Sciences, 100101 Beijing, China

Markov random field (MRF) provides a useful model for integrating contextual information into remote sensing image classification. However, there are two limitations when using the conventional MRF model in hyperspectral image classification. First, the maximum likelihood classifier used in MRF to estimate the spectral-based probability needs accurate estimation of covariance matrix for each class, which is often hard to obtain with a small number of training samples for hyperspectral imagery. Second, a fixed spatial neighboring impact parameter for all pixels causes overcorrection of spatially high variation areas and makes class boundaries blurred. This paper presents an improved method for integrating a support vector machine (SVM) and Markov random field to classify the hyperspectral imagery. An adaptive spatial neighboring impact parameter is assigned to each pixel according to its spatial contextual correlation. Experimental results of a hyperspectral image show that the classification accuracy from the proposed method has been improved compared to those from the conventional MRF model and pixel-wise classifiers including the maximum likelihood classifier and SVM classifier.

History

Received Nov 29, 2010
Accepted Jun 21, 2011
Revised May 30, 2011
Published online Jul 15, 2011
Corrected Aug 08, 2011

Digital Object Identifier

http://dx.doi.org/10.1117/1.3609847

Citation

Shanshan Li, Bing Zhang, Dongmei Chen, Lianru Gao and Man Peng, "Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery", J. Appl. Remote Sens. 5, 053538 (Jul 15, 2011); http://dx.doi.org/10.1117/1.3609847

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Errata: Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery
Shanshan Li et al.
J. Appl. Remote Sens. 5, 050101 (2011)JARSC4000005000001050101000001