期刊
JOURNAL OF SENSORS
卷 2015, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2015/258619
关键词
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资金
- National Natural Science Foundation of China [61371165, 61302164]
- 973 Program of China [2011CB706900]
- Program for New Century Excellent Talents in University [NCET-11-0711]
- Interdisciplinary Research Project in Beijing University of Chemical Technology
- Beijing Higher Education Young Elite Teacher Project [YETP0501, YETP0500]
Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
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