2D-DFrFT Based Deep Network for Ship Classification in Remote Sensing Imagery

Abstract

Ship classification in optical remote sensing images is a fundamental but challenging problem with wide range of applications. Deep convolutional neural network (CNN) has shown excellent performance in object classification; however, limited available training samples prevent CNN for ship classification. In this paper, a novel ship-classification framework consisting of two-branch CNN and two dimensional discrete fractional Fourier transform (2D-DFrFT) is proposed. Firstly, the amplitude and phase information of ship image in 2D-DFrFT is extracted. Due to the fact that different orders of 2D-DFrFT have different contribution on the process of feature extraction of ship image. Thus the amplitude (M) and phase (P) value obtained in different orders are regarded as the input of two-branch CNN that can learn the high-level features automatically. After multiple features learning, decision-level fusion is adopted for final classification. The remote sensing image data, named as BCCT200-resize, is utilized for validation. Compared to the existing state-of-art algorithms, the proposed method has superior performance.