Papers › SIAMESE NETWORK BASED METRIC LEARNING FOR SAR TARGET CLASSIFICATION
SIAMESE NETWORK BASED METRIC LEARNING FOR SAR TARGET CLASSIFICATION
Zongxu Pan1, 2*, Xianjie Bao1, Yueting Zhang1, Bowei Wang1, Quanzhi An1, 3, and Bin Lei1, 2
A Siamese network based metric learning method is proposed for SAR target classification with few training samples. The network consists of two identical CNNs sharing the weights. Different from classification networks that predict the category of one sample, the Siamese network implements a metric learning to measure the similarity between two samples. Since the input is the sample pair, the amount of training data dramatically increases which contributes to training a better network. When generating the pairs, a hard negative mining scheme is proposed for improving the performance. To avoid computing the similarity between the test sample and each training sample at the test stage, which is time consuming, a two stages scheme is employed with an additional classification network taking the output of the single branch of Siamese network as the input and predicting the category. Experiments on the MSTAR dataset validate the effectiveness of the proposed method.
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