title: An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery.
abstract: With the rapid development of spaceborne synthetic aperture radar (SAR) and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of great importance. Focusing on practical problems of ship detection, this paper presents a highly adaptive ship detection scheme for spaceborne SAR imagery. It is able to process a wide range of sensors, imaging modes and resolutions. Two main stages are identified in this paper, namely: ship candidate detection and ship discrimination. Firstly, this paper proposes an adaptive land masking method using ship size and pixel size. Secondly, taking into account the imaging mode, incidence angle, and polarization channel of SAR imagery, it implements adaptive ship candidate detection in spaceborne SAR imagery by applying different strategies to different resolution SAR images. Finally, aiming at different types of typical false alarms, this paper proposes a comprehensive ship discrimination method in spaceborne SAR imagery based on confidence level and complexity analysis. Experimental results based on RADARSAT-1, RADARSAT-2, TerraSAR-X, RS-1, and RS-3 images demonstrate that the adaptive scheme proposed in this paper is able to detect ship targets in a fast, efficient and robust way.
authors: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. luckight@163.com.
authors: Xiangguang
authors: X
authors: Leng
authors: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. jikefeng@nudt.edu.cn.
authors: Kefeng
authors: K
authors: Ji
authors: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. slzhou@nudt.edu.cn.
authors: Shilin
authors: S
authors: Zhou
authors: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. xingxiangwei@nudt.edu.cn.
authors: Xiangwei
authors: X
authors: Xing
authors: School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China. hxzou2008@163.com.
authors: Huanxin
authors: H
authors: Zou
keywords: adaptive ship detection; confidence probability; constant false alarm rate (CFAR); ship candidate detection; ship discrimination; synthetic aperture radar (SAR)