# SBNN: A Searched Binary Neural Network for SAR Ship Classification
###### Abstract  
The synthetic aperture radar (SAR) for ocean surveillance missions requires low latency and light weight inference. This paper proposes a novel small-size Searched Binary Network (SBNN), with network architecture search (NAS) for ship classification with SAR. In SBNN, convolution operations are modified by binarization technologies. Both input feature maps and weights are quantized into 1-bit in most of the convolution computation, which significantly decreases the overall computational complexity. In addition, we propose a patch shift processing, which can adjust feature maps with learnable parameters at spatial level. This process enhances the performance by reducing the information irrelevant to the targets. Experimental results on the OpenSARShip dataset show the proposed SBNN outperforms both binary neural networks from computer vision and CNN-based SAR ship classification methods. In particular, SBNN shows a great advantage in computational complexity. radar information intelligent computing; binary neural networks; convolutional neural networks; ship classification; deep learning +
Footnote †: journal: Applied sciences  
respectively extracted by the same backbone which is also adapted from VGGNet. Then, predictions of the categories can be obtained with an improved classifier and fused features. The Dual-Polarization classification network is trained with a novel hybrid channel feature loss. However, the bloated architecture and the high requirement on input resolution make this method have an extremely high computational complexity. A deep learning network, HOG-ShipCLSNet, is proposed with histogram of oriented gradient and principal component analysis for SAR ship classification [16]. In HOG-ShipCLSNet, principal component analysis extracts features in histograms of oriented gradient processed from SAR images while a CNN extracting features in original SAR images. This method achieves good results on both the Sentinel-1 three categories classification task and the Gaofen-3 six categories classification task. Due to the complex and massive computation, it is difficult to implement HOG-ShipCLSNet on mobile platforms. A Squeeze-and-Excitation Laplacian pyramid network, SE-LPN-DPFF, is proposed for Dual-Polarization SAR ship classification. SE-LPN-DPFF uses the VV channel, the VH channel and the polarization coherence as input. The network architecture includes Dual-Polarization feature fusion, Squeeze-and-Excitation and Laplacian pyramid. Although SE-LPN-DPFF reaches a high accuracy, its large predication latency and dependency on Dual-Polarization data limits the practical application potential. Following the concept of the data-driven model, a novel small-size Searched Binary Neural Network (SBNN) for SAR ship classification is proposed. Our network has advantages of high classification accuracy, low computational complexity and low hardware implementation requirements, which means SBNN is friendly to mobile or small SAR platforms. We summarize our main contributions as follows:  
1. A Searched Binary Neural Network with a decreased computational complexity for SAR ship classification is proposed. Most of the existing SAR ship classification networks are hand-crafted or adapted from computer vision general network. Due to the large complexity of these networks, it is difficulty to implement them on mobile or embedded devices. In this paper, only normalized SAR images are used as input. Then, features are extracted by SBNN, where most of convolution operators are binarized into 1-bit. Compared with existing floating-point SAR ship classification methods, SBNN keeps the sensing capability and classification accuracy at the same level as the state-of-the-art models and achieves a 15\(\times\) reduction in the number of multiply-accumulate calculations (MAdds). 2. Searching a target network for SAR ship classification with network architecture search (NAS) is researched in this paper. The searched CNN gives a higher accuracy than adapted or hand-crafted SAR classification CNNs.