A Segmentation based CFAR detection algorithm using truncated statistics

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Ding, Tao, Doulgeris, Anthony Paul, Brekke, Camilla
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2016
• Subjects: VDP::Matematikk og Naturvitenskap: 400::Geofag: 450; VDP::Mathematics and natural science: 400::Geosciences: 450; Arctic
• Online Access: https://hdl.handle.net/10037/10602; https://doi.org/10.1109/TGRS.2015.2506822

Manuscript. (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published version available in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, May 2016 Target detection in nonhomogeneous sea clutter environments is a complex and challenging task due to the capture effect from interfering outliers and the clutter edge effect from background intensity transitions. For synthetic aperture radar (SAR) measurements, those issues are commonly caused by multiple targets and meteorological and oceanographic phenomena, respectively. This paper proposes a segmentation-based constant false-alarm rate (CFAR) detection algorithm using truncated statistics (TS) for multilooked intensity (MLI) SAR imagery, which simultaneously addresses both issues. From our previous work, TS is a useful tool when the region of interest (ROI) is contaminated by multiple nonclutter pixels. Within each ROI confined by the reference window, the proposed scheme implements an automatic image segmentation algorithm, which performs a finite mixture model estimation with a modified expectation-maximization algorithm. Data truncation is applied here to exclude all possible statistically interfering classes, and sample modeling is based upon the truncated two-parameter gamma model. Next, CFAR detection is conducted pixel by pixel, utilizing the statistical information obtained from the segmentation process within the local reference window. The segmentation-based CFAR detection scheme is examined with real Radarsat-2 MLI SAR imagery. Compared with the conventional CFAR detection approaches, our proposal provides improved background clutter modeling and robust detection performance in nonhomogeneous clutter environments.