SAR Image Target Recognition Based on Across Convolution Network Feature Fusion

doi:10.16182/j.issn1004731x.joss.19-0609

Abstract

Abstract: Convolutional neural networks have been widely used in the field of synthetic aperture radar image target recognition. Based on the LeNet-5 neural network model, a SAR image target recognition method are initialized across convolution network feature fusion is proposed. The LeNet-5 network parameters on the basis of MNIST handwritten data. The deep and shallow features of the SAR image are extracted, and the principal component analysis on the shallow features is performed to obtain key category information. Deep features and shallow features are fused and are classified and recognised by sent to collaborative representation. Experimental results show that the method can achieve 98% average recognition rate without expanding the training samples.

Key words: synthetic aperture radar, LeNet-5 neural network, collaborative representation classification, deep feature

CLC Number:

TP183
TP391

Feng Xinyang, Shao Chao. SAR Image Target Recognition Based on Across Convolution Network Feature Fusion[J]. Journal of System Simulation, 2021, 33(3): 554-561.

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