Sociology

Pattern recognition (psychology)

Convolution (computer science)

Data mining

Normalization (sociology)

Automatic target recognition

Artificial intelligence

Anthropology

Computer science

Feature extraction

Synthetic aperture radar

Artificial neural network

IEEE Transactions on Geoscience and Remote Sensing

In synthetic aperture radar automatic target recognition (SAR-ATR), target information is usually propagated and reserved in complex-valued form, namely magnitude information and phase information. However, most of the existing SAR target recognition methods only focus on real-valued (magnitude information) calculations and ignore the phase information of targets, yielding poor recognition performance. To overcome this limitation, this paper proposes a multi-stream feature fusion SAR target recognition method based on complex-valued operations, called MS-CVNets, to utilize the phase information of the target effectively. First of all, a series of complex-valued operation blocks are constructed to satisfy the network training in the complex field, such as complex convolution, complex batch normalization, complex activation, complex pooling, complex full connection, etc. Besides, a multi-stream structure is employed by applying different convolution kernels to extract multi-scale information of targets, further enhancing the representation ability of the model. Experimental results on the MSTAR dataset illustrate that, compared with current state-of-the-art real-valued based models, MS-CVNets can achieve better recognition results under both standard operating conditions (SOC) and extended operating conditions (EOC), validating the effectiveness and superiority of the proposed method.

SAR Automatic Target Recognition Method Based on Multi-Stream Complex-Valued Networks