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Remote Sens. 2018, 10(1), 110;

Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach

School of Artificial Intelligence, Xidian University, Xi’an 710071, Shaanxi, China
GST at NOAA/NESDIS, College Park, MD 20740, USA
Authors to whom correspondence should be addressed.
Received: 21 November 2017 / Revised: 10 January 2018 / Accepted: 12 January 2018 / Published: 15 January 2018
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In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image classification method based on multilayer autoencoders and self-paced learning (SPL) is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods. View Full-Text
Keywords: PolSAR; classification; multilayer autoencoders; self-paced learning PolSAR; classification; multilayer autoencoders; self-paced learning

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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 (CC BY 4.0).

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Chen, W.; Gou, S.; Wang, X.; Li, X.; Jiao, L. Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach. Remote Sens. 2018, 10, 110.

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