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Journal of Applied Remote Sensing

Representation-based classifications with Markov random field model for hyperspectral urban data
Author(s): Mingming Xiong; Fan Zhang; Qiong Ran; Wei Hu; Wei Li
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Paper Abstract

Recently, representation-based classifications have gained increasing interest in hyperspectral imagery, such as the newly proposed sparse-representation classification and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic support vector machine. However, all these representation-based methods were originally designed to be pixel-wise classifiers which only consider the spectral signature while ignoring the spatial-contextual information. A Markov random field (MRF), providing a basis for modeling contextual constraints, has currently been successfully applied for hyperspectral image analysis. We mainly investigate the benefits of combining these representation-based classifications with an MRF model in order to acquire better classification results. Two real hyperspectral images are used to validate the proposed classification scheme. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art approaches. For example, NRS-MRF performed with an accuracy of 94.92% for the Reflective Optics System Imaging Spectrometer data with 60 training samples per class, while the original NRS obtained an accuracy of 81.95%, an improvement of approximately 13%.

Paper Details

Date Published: 28 July 2014
PDF: 12 pages
J. Appl. Remote Sens. 8(1) 085097 doi: 10.1117/1.JRS.8.085097
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Mingming Xiong, Beijing Univ. of Chemical Technology (China)
Fan Zhang, Beijing Univ. of Chemical Technology (China)
Qiong Ran, Beijing Univ. of Chemical Technology (China)
Wei Hu, Beijing Univ. of Chemical Technology (China)
Wei Li, Beijing Univ. of Chemical Technology (China)


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