Ship Classification Based on Superstructure Scattering Features in SAR Images

• Published in: IEEE geoscience and remote sensing letters Vol. 13; no. 5; pp. 616 - 620
• Main Authors: Jiang, Mingzhe, Yang, Xuezhi, Dong, Zhangyu, Fang, Shuai, Meng, Junmin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Backscatter; Classification; Containers; Feature extraction; Marine vehicles; Ratio of dimensions (RoD); Scattering; Sensors; ship classification; Synthetic aperture radar; synthetic aperture radar (SAR); synthetic aperture radar (SAR); ship classification; Ratio of dimensions (RoD)
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2016.2514482

This letter presents a novel method for ship classification that uses synthetic-aperture-radar images to distinguish ships based on superstructure scattering features. The ratio of dimensions, which combines the 2-D and 3-D properties of scattering, is explored as an effective and credible means to describe the scattering features of ships. The proposed method consists of three main stages: 1) ship isolation from the sea; 2) parametric vector (F) estimation; and 3) categorization using a support vector machine (SVM) classifier. To depict ship features more accurately and reduce feature redundancy, we propose employing peak extraction to divide a ship into bow, middle, and stern instead of into three equal parts. The classification method is tested with RadarSat-2 images, and ground-truth information is supplied by an automatic identification system. The experimental results show that the proposed method can achieve satisfactory ship-classification performance compared with existing methods, with an overall accuracy exceeding 80%.