Deep CNN with Multi-Scale Rotation Invariance Features for Ship Classification

With the rapid development of target tracking technology, how to efficiently take advantage of useful information from optical images for ship classification becomes a challenging problem. In this paper, a novel deep learning framework fused with low-level features is proposed. Deep convolutional neural network (CNN) has been popularly used to capture structural information and semantic context because of the ability of learning high-level features; however, lacking of capability to deal with global rotation in large-scale image and losing some important information in bottom layers of the CNN limit its performance in extracting multi-scales rotation invariance features. Comparatively, some classic algorithms, such as Gabor filter or multiple scales completed local binary patterns, can effectively capture low-level texture information. In the proposed framework, low-level features are combined with high-level features obtained by deep CNN. The fused features are further fed into a typical support vector machine classifier. The proposed strategy achieves average accuracy of 98.33% on the BCCT200-RESIZE data and 88.00% on the challenging VAIS data, which demonstrates its superior classification performance when compared with some state-of-the-art methods.