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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