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Self-Paced Convolutional Neural Network for PolSAR Images Classification

1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
2
MathWorks, Natick, MA 01760, USA
3
GST at NOAA/NESDIS, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(4), 424; https://doi.org/10.3390/rs11040424
Received: 3 February 2019 / Revised: 14 February 2019 / Accepted: 15 February 2019 / Published: 19 February 2019
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Abstract

Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed. In our method, each pixel is denoted by a 3-dimensional tensor block formed by its scattering intensity values on four channels, Pauli’s RGB values and its neighborhood information. Then, we train SPCNN to extract the channel-spatial features and obtain the classification results. Inspired by self-paced learning, SPCNN learns the easier samples first and gradually involves more difficult samples into the training process. This learning mechanism can make network converge to better values. The proposed method achieved state-of-the-art performances on four real PolSAR dataset. View Full-Text
Keywords: PolSAR classification; self-paced learning; self-paced convolutional neural network PolSAR classification; self-paced learning; self-paced convolutional neural network
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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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Jiao, C.; Wang, X.; Gou, S.; Chen, W.; Li, D.; Chen, C.; Li, X. Self-Paced Convolutional Neural Network for PolSAR Images Classification. Remote Sens. 2019, 11, 424.

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