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Remote Sens. 2017, 9(9), 907; doi:10.3390/rs9090907

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

1,2,3
,
2,3
and
2,3,*
1
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China
2
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Received: 7 July 2017 / Revised: 20 August 2017 / Accepted: 28 August 2017 / Published: 31 August 2017
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Abstract

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway. View Full-Text
Keywords: SAR target recognition; deep CNNs; transfer learning; stacked convolutional auto-encoders SAR target recognition; deep CNNs; transfer learning; stacked convolutional auto-encoders
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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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Huang, Z.; Pan, Z.; Lei, B. Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data. Remote Sens. 2017, 9, 907.

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