Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 54, Issue 12, Pages 7416-7430Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2016.2603190
Keywords
Hyperspectral image (HSI); probabilistic fusion; spectral-spatial classification; superpixel segmentation
Categories
Funding
- National Natural Science Fund of China for Distinguished Young Scholars [61325007]
- National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]
- Science and Technology Plan Project Fund of Hunan Province [2015WK3001]
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A novel hyperspectral image (HSI) classification method by the probabilistic fusion of pixel-level and superpixel-level classifiers is proposed. Generally, pixel-level classifiers based on spectral information only may generate salt and pepper result in the classification map since spatial correlation is not considered. By incorporating spatial information in homogeneous regions, the superpixel-level classifiers can effectively eliminate the noisy appearance. However, the classification accuracy will be deteriorated if undersegmentation cannot be fully avoided in superpixel-based approaches. Therefore, it is proposed to adaptively combine both the pixel-level and superpixel-level classifiers, to improve the classification performance in both homogenous and structural areas. In the proposed method, a support vector machine classifier is first applied to estimate the pixel-level class probabilities. Then, superpixel-level class probabilities are estimated based on a joint sparse representation. Finally, the two levels of class probabilities are adaptively combined in a maximum a posteriori estimation model, and the classification map is obtained by solving the maximum optimization problem. Experimental results on real HSI images demonstrate the superiority of the proposed method over several well-known classification approaches in terms of classification accuracy.
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