Ship Classification in Moderate-Resolution SAR Image by Naive Geometric Features-Combined Multiple Kernel Learning

Compared with the high-resolution synthetic aperture radar (SAR) image, a moderate-resolution SAR image can offer wider swath, which is more suitable for maritime ship surveillance. Taking into account the amount of information in a moderate-resolution SAR image and the stability of feature extraction, we propose naive geometric features (NGFs) for ship classification. In contrast to the strictly defined geometric features (SGFs), the extraction of NGFs is very simpler and efficient. And more importantly, the NGFs are enough to reveal the essential difference between different types of ships for classification. To fuse various NGFs with different physical properties and discriminability, the multiple kernel learning (MKL) is utilized to learn the combination weights, rather than assigning the same weight to all features as usually applied by the traditional support vector machines (SVMs). The comprehensive experiments validate that: (1) the performance of the proposed NGF-combined MKL outperforms that of NGF-combined SVM by 3.4% and is very close to that obtained by SGF-combined MKL and (2) in terms of classifying ships in a moderate-resolution SAR image, NGFs are more feasible than scattering features.