Synthetic aperture radar (SAR) target recognition has important research value in both military and civil fields. However, due to the high cost of SAR data acquisition and the small number of samples, the traditional convolutional neural network has insufficient ability to extract target features and low accuracy. This paper proposes a classification model combining convolutional attention and capsule network, which uses multi-dimensional vector neurons in the capsule network to represent more features of the target. At the same time, considering the lack of target feature information under the condition of small sample information, in order to improve the learning efficiency of the neural network, attention mechanism is added to the capsule network to guide the classification network to repeat the classification by learning the importance of different features, focusing on the features that contribute more to the classification results, and weakening the features that contribute less to the classification results. The experimental results on MSTAR data set and real vehicle data set show that the accuracy of the proposed algorithm is higher than those of the traditional convolution neural network and capsule network algorithm.
HUO Xinyi
,
LI Yanlei
,
CHEN Longyong
,
ZHANG Fubo
,
SUN Wei
. SAR few-sample target recognition method based on convolutional block attention module and capsule network[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(6)
: 783
-792
.
DOI: 10.7523/j.ucas.2021.0022
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