Unsupervised Ship Detection in SAR Imagery Based on Energy Density-Induced Clustering

Abstract

Article Unsupervised Ship Detection in SAR Imagery Based on Energy Density-Induced Clustering Zifeng Yuan 1, Yu Li 1,*, Yu Liu 1, Jiale Liang 1, and Yuanzhi Zhang 2,3 1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 2 School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China 3 Key Laboratory of Lunar and Deep Space Exploration, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China * Correspondence: yuli@bjut.edu.cn Received: 6 March 2023 Accepted: 24 April 2023 Published: 26 September 2023 Abstract: Intelligent recognition of maritime ship targets from synthetic aperture radar (SAR) imagery is a hot research issue. However, interferences such as the strong sea clutter, sidelobe, small ship size and weak backscattered signal continually affect the detection results. To address this problem, a novel unsupervised machine learning-based ship detection algorithm, named energy density-induced clustering (EDIC), is proposed in this paper. It is discovered that the singular values between ship targets and interference signals are significantly different in a local region because of their various concentration degrees of signal energy intensity. Accordingly, in this study, two novel energy density features are proposed based on the singular value decomposition in order to effectively highlight the ship targets and suppress the interference. The proposed novel energy density features have the advantage of clearly distinguishing ship targets from sea surfaces regardless of the effects of interferences. To test the performance of the proposed features, unsupervised K-means clustering is conducted for obtaining ship detection results. Compared with the classical and state-of-the-art SAR ship detectors, the proposed EDIC method generally yields the best performance in almost all tested sea sample areas with different kinds of interferences, in terms of both detection accuracy and processing efficiency. The proposed energy density-based feature extraction method also has great potential for supervised classification using neural networks, random forests, etc.