title: A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery.
abstract: Traditional constant false alarm rate (CFAR) detectors only use the contrast information between ship targets and clutter, and they suffer probability of detection (PD) degradation in multiple target situations. This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data.
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China. aijiaqiu@aliyun.com.
authors.forename: Jiaqiu
authors.initials: J
authors.lastname: Ai
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China. xzyang@hfut.edu.cn.
authors.forename: Xuezhi
authors.initials: X
authors.lastname: Yang
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China. zhoufang@hfut.edu.cn.
authors.forename: Fang
authors.initials: F
authors.lastname: Zhou
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China. dzyhfut@hfut.edu.cn.
authors.forename: Zhangyu
authors.initials: Z
authors.lastname: Dong
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China. LuJia@hfut.edu.cn.
authors.forename: Lu
authors.initials: L
authors.lastname: Jia
authors.affiliation: College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Yudao Avenue, Nanjing 210016, China. yanhe@nuaa.edu.cn.
authors.forename: He
authors.initials: H
authors.lastname: Yan
keywords: 2D joint log-normal distribution; SAR; adaptively truncated clutter statistics; correlation-based joint CFAR; ship detection