期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 8, 期 5, 页码 973-977出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2011.2145353
关键词
Hyperspectral imagery; Markov random field (MRF); relative homogeneity index (RHI); support vector machine (SVM)
类别
资金
- National Basic Research Program of China (973 Program) [2009CB723902]
- National High Technology Research and Development (863 Program) [2008AA12Z113]
- National Natural Science Foundation of China [40901225]
An adaptive Markov random field (MRF) approach is proposed for classification of hyperspectral imagery in this letter. Themain feature of the proposed method is the introduction of a relative homogeneity index for each pixel and the use of this index to determine an appropriate weighting coefficient for the spatial contribution in the MRF classification. In this way, overcorrection of spatially high variation areas can be avoided. Support vector machines are implemented for improved class modeling and better estimate of spectral contribution to this approach. Experimental results of a synthetic hyperspectral data set and a real hyperspectral image demonstrate that the proposed method works better on both homogeneous regions and class boundaries with improved classification accuracy.
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