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Journal of Sensors
Volume 2016, Article ID 3632943, 10 pages
Research Article

Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images

1Faculty of Mechanical and Electronic Information, China University of Geosciences, Wuhan, Hubei 430074, China
2Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China

Received 25 December 2014; Revised 11 May 2015; Accepted 21 June 2015

Academic Editor: Jonathan C.-W. Chan

Copyright © 2016 Chen Xing 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.


Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE) method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR) approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU) as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR) can achieve higher accuracies than the popular support vector machine (SVM) classifier.