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SAR Automatic Target Recognition Method Based on Multi-Stream Complex-Valued Networks
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DOI：10.1109/TGRS.2022.3177323.png)
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Abstract
En 中文
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 article proposes a multi-stream (MS) feature fusion SAR target recognition method based on complex-valued operations, called MS complex-valued networks (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, and complex full connection. Besides, an MS structure is employed by applying different convolution kernels to extract multiscale information of targets, further enhancing the representation ability of the model. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset illustrate that compared with the current state-of-the-art real-valued based models, MS-CVNets can achieve better recognition results under both standard operating conditions (SOCs) and extended operating conditions (EOCs), validating the effectiveness and superiority of the proposed method.
Keywords:
Synthetic aperture radar
Target recognition
Task analysis
Radar polarimetry
Feature extraction
Convolution
Training
Automatic target recognition (ATR)
complex-valued network (CVNet)
deep learning (DL)
feature fusion
multi-stream (MS) structure
synthetic aperture radar (SAR)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W