Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22331
Title: Spatial-attraction-based Markov random field approach for classification of high spatial resolution multispectral imagery
Authors: Zhang, H
Shi, W 
Wang, Y
Hao, M
Miao, Z
Keywords: Classification
High spatial resolution multispectral imagery (HSRMI)
Markov random field (MRF)
Spatial attraction
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE geoscience and remote sensing letters, 2014, v. 11, no. 2, p. 489-493 How to cite?
Journal: IEEE geoscience and remote sensing letters 
Abstract: This letter presents a novel spatial-attraction-based Markov random field (MRF) (SAMRF) approach for high spatial resolution multispectral imagery (HSRMI) classification. First, the initial class label and class membership for each pixel are obtained by applying the maximum likelihood classifier (MLC) classification for the HSRMI. Second, to reduce the oversmooth classification in the traditional MRF, an adaptive weight MRF model is introduced by integrating the spatial attraction model into the traditional MRF. Finally, the initial classification map, generated in the first step, will be refined though the SAMRF regularization. Two different experiments were performed to evaluate the performance of the SAMRF, in comparison with standard MLC and MRF. Experimental results indicate that the SAMRF method achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.
URI: http://hdl.handle.net/10397/22331
ISSN: 1545-598X
DOI: 10.1109/LGRS.2013.2268968
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