期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 53, 期 3, 页码 1490-1503出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2014.2344442
关键词
alternating direction method of multipliers (ADMM); hyperspectral classification (HC); sparse multinomial logistic regression (SMLR); spatially adaptive TV constraint
类别
资金
- National Natural Science Foundation of China [61101194, 61071146]
- Jiangsu Provincial Natural Science Foundation of China [BK2011701]
- Research Fund for the Doctoral Program of Higher Education of China [20113219120024, 20123219120043]
- Project of China Geological Survey [1212011120227]
- Jiangsu Planned Projects for Postdoctoral Research Funds [0901008B]
- National Scientific Equipment Developing Project of China [2012YQ050250]
This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial information. Under the maximum a posteriori framework, we propose a supervised classification model which includes a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm, named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.
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