Research Papers

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

[+] Author Affiliations
Shanshan Li

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

Bing Zhang

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

Lianru Gao

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

Man Peng

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

J. Appl. Remote Sens. 5(1), 053538 (July 15, 2011). doi:10.1117/1.3609847
History: Received November 29, 2010; Revised May 30, 2011; Accepted June 21, 2011; Published July 15, 2011; August 08, 2011; Online July 15, 2011
Text Size: A A A

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.

Figures in this Article
© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

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(1), 053538 (July 15, 2011). ; http://dx.doi.org/10.1117/1.3609847


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.