Feature learning for SAR images using convolutional neural network

Convolutional neural network (CNN) has been widely used in many research areas due to its powerful ability of feature learning. In this paper, the powerful ability of feature learning in CNN is explored by constructing a novel convolutional network (ConvNet) for SAR image processing. The proposed ConvNet is firstly trained under classification task, in which effective features can be learned automatically from the training data. Specifically, data argument is adopted to overcome the small-sample-problem in SAR images. When well-trained, the proposed ConvNet can be directly used for feature extraction of other images, even though their classes maybe not used in the training. Experimental results on benchmark MSTAR data set demonstrate that the proposed ConvNet is effective for classification of SAR images, and the features learned from it are more effective than traditional hand-crafted features in SAR image processing.