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Remote Sens. 2016, 8(4), 355; doi:10.3390/rs8040355

Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
4
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Magaly Koch and Prasad S. Thenkabail
Received: 24 February 2016 / Revised: 15 April 2016 / Accepted: 20 April 2016 / Published: 23 April 2016
View Full-Text   |   Download PDF [4168 KB, uploaded 23 April 2016]   |  

Abstract

This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial information of the image pixels is modeled using an adaptive Markov random field (MRF) method. Finally, the maximum posterior probability classification is computed via the simulated annealing (SA) optimization algorithm. The combination of subspace-based SVMs and adaptive MRFs is the main contribution of this paper. The resulting methods, called SVMsub-eMRF and SVMsub-aMRF, were experimentally validated using two typical real hyperspectral data sets. The obtained results indicate that the proposed methods demonstrate superior performance compared with other classical hyperspectral image classification methods. View Full-Text
Keywords: hyperspectral image classification; support vector machines (SVMs); subspace projection method; adaptive Markov random field hyperspectral image classification; support vector machines (SVMs); subspace projection method; adaptive Markov random field
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yu, H.; Gao, L.; Li, J.; Li, S.S.; Zhang, B.; Benediktsson, J.A. Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields. Remote Sens. 2016, 8, 355.

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