HDSS-Net: A Novel Hierarchically Designed Network With Spherical Space Classifier for Ship Recognition in SAR Images

Abstract

Ship recognition in synthetic aperture radar (SAR) images is essential for many applications in maritime surveillance tasks. Recently, convolutional neural network (CNN)-based methods tend to be the mainstream in SAR recognition. Though considerable developments have been achieved, there are still several challenging issues toward superior ship recognition performance: 1) Ships have a large variance in size, making it difficult to recognize ships by using a single scale features of CNN. 2) The SAR ship’s large aspect ratio presents an obvious geometric characteristic. However, standard convolution is limited by the fixed convolution kernel, which is less effective in processing elongated SAR ships. 3) Existing CNN classifiers with softmax loss are less powerful to deal with intraclass diversity and interclass similarity in SAR ships. In this paper, we propose a task-specific hierarchically designed network with a spherical space classifier (HDSS-Net) to alleviate the above issues. Firstly, to realize SAR ship recognition with large size variation, a feature aggregation module (FAM) is designed for obtaining a feature pyramid that has strong representational power at all scales. Secondly, a FeatureBoost module (FBM) is devised to provide rectangular receptive fields to refine the features generated by FAM. Finally, a novel spherical space classifier (SSC) is proposed to expand the interclass margin and compress the intraclass feature distribution by fully taking advantage of the property of spherical space. The experimental results on two benchmark datasets (OpenSARShip and FUSAR-Ship) jointly show that the proposed HDSS-Net performs better than classic CNN methods and novel SAR ship recognition CNN methods.