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SAR Target Image Classification Based on Transfer Learning and Model Compression
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DOI：10.1109/LGRS.2018.2876378.png)
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When convolutional neural networks (CNNs) are applied to the synthetic aperture radar (SAR) image classification, they are prone to overfitting due to scarce SAR image data, and CNNs require a large amount of storage and long computing time, so it is difficult to deploy them on resource constrained devices. This letter proposes a simple and feasible approach that can effectively solve these problems. First, the convolutional layers of the pretrained model on the Image Net data set are transferred, and a new convolutional layer and global pooling layer are added afterward. Then, fine-tuning is performed on the new network from the SAR image data set. Finally, a filter based pruning method is used on the convolutional layers to obtain a compact network. Compared with the all-convolutional network (A-ConvNets) which is the state-of-the-art method on the moving and stationary target acquisition and recognition data set, our method achieves about 3.6 x speedup during forward propagation and 3.7 x compression of the parameters, with only a 1.42% decrease in the accuracy.
Keyword:
Convolutional neural networks (CNNs)
model compression
synthetic aperture radar (SAR)
transfer learning
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