Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery

作者：Zhang Bing^*; Li Shanshan; Jia Xiuping; Gao Lianru; Peng Man

来源：IEEE Geoscience and Remote Sensing Letters, 2011, 8(5): 973-977.

DOI：10.1109/LGRS.2011.2145353

• Hyperspectral imagery
• Markov random field (MRF)
• relative homogeneity index (RHI)
• support vector machine (SVM)

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.

• 出版日期2011-9
• 单位中国科学院遥感与数字地球研究所; 中国科学院对地观测与数字地球科学中心