Multi-Scale SAR Ship Classification with Convolutional Neural Network
Xiaowo Xu, Xiaoling Zhang, Tianwen Zhang
Abstract
Ship classification in Synthetic Aperture Radar (SAR) images is significant but its application based on Convolutional Neural Network (CNN) has not been adequately studied. Considering that there will be the loss of SAR ship spatial information as the network deepening in CNN, which is a great obstacle for the further improvement of algorithm accuracy. Thus, to deal with the problem, in this paper, a novel multi-scale CNN (MS-CNN) is proposed. MS-CNN can utilize the multi-scale features to enhance the feature expression ability by the following three steps, namely flattening, integrating and classifying. As a result, the experiments on the OpenSARShip dataset show that MS-CNN can increase the classification accuracy by 4.81% than benchmark network.