Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77445
Title: Remote sensing image classification based on stacked denoising autoencoder
Authors: Liang, P
Shi, W 
Zhang, X
Issue Date: 2018
Source: Remote sensing, Jan. 2018, v. 10, no. 1, 16, p. 1-12
Abstract: Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP) neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1) remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.
Keywords: Back Propagation neural network
Deep learning
Land cover classification
Stacked denoising autoencoder
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Remote sensing 
EISSN: 2072-4292
DOI: 10.3390/rs10010016
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Liang, P., Shi, W., & Zhang, X. (2018). Remote sensing image classification based on stacked denoising autoencoder. Remote Sensing, 10(1), (Suppl. ), 16, - is available athttps://dx.doi.org/10.3390/rs10010016
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