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| Similarity Constrained Deep Belief Networks with Application to SAR Image Target Recognition |
| DING Jun LIU Hongwei CHEN Bo FENG Bo WANG Yinghua |
| (National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China) |
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Abstract Feature extraction is a key step in SAR image target recognition. The existence of speckle and discontinuity makes the conventional methods for natural images difficult to apply. Although Deep Belief Networks (DBNs) can be used to learn feature representations automatically, they work essentially in an unsupervised way, and hence the learned features are task-irrelevant. A new Boltzmann machine called Similarity constrained Restricted Boltzmann Machines (SRBMs) is proposed, which injects the supervised information into learning process through constraint on the similarity of feature vectors. Furthermore, a deep architecture named Similarity constrained DBNs (SDBNs) is constructed by layer-wise stacking of SRBMs. Experimental results show the proposed SDBN is superior to DBN and PCA in SAR image target recognition.
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Received: 26 March 2015
Published: 17 November 2015
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| Fund: The National Natural Science Foundation of China (61372132, 61201292), The Program for New Century Excellent Talents (NCET-13-0945), The Program for Young Thousand Talent by Chinese Central Government |
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Corresponding Authors:
CHEN Bo
E-mail: bchen@mail.xidian.edu.cn
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