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Journal of Sensors
Volume 2015, Article ID 258619, 12 pages
Research Article

Deep Convolutional Neural Networks for Hyperspectral Image Classification

1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 10029, China
2Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China
3Department of Aerospace Engineering Sciences, University of Colorado, Boulder, CO 80309, USA

Received 23 November 2014; Accepted 22 January 2015

Academic Editor: Tianfu Wu

Copyright © 2015 Wei Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.