CNN-based ship classification method incorporating SAR geometry information

This paper proposes a ship classification method for synthetic aperture radar (SAR) images, which incorporates SAR geometry information into a convolutional neural network (CNN). Most of the conventional methods for ship classification employ appearance-based features. These features extracted from SAR image are not robust to a geometry change because the geometry difference significantly changes the appearance of target objects in SAR images. CNN has a potential to handle the variations in appearance. However, it requires huge training data, which is rarely available in SAR, to implicitly learn geometry-invariant features. In this paper, we propose a CNN-based ship classification method incorporating SAR geometry information. We focus on the incident angle information that is included in a metadata because incident angle change directly affects the appearance of objects. The proposed method enables a network to learn a relationship between the appearance and SAR geometry by utilizing the incident angle information as a condition. Experimental results show that the proposed method improves the classification accuracy by 1.1% as compared to the conventional CNN, which does not utilize incident angle information. Furthermore, our method requires 25% less training data as compared to the conventional CNN to achieve 70% classification accuracy.