Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30199
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter
Author(s): Liu, Tao
Zhang, Jiafeng
Gao, Gui
Yang, Jian
Marino, Armando
Contact Email: armando.marino@stir.ac.uk
Keywords: Constant false alarm rate (CFAR)
polarimetric whitening filter (PWF)
ship detection
synthetic aperture radar.
Issue Date: 26-Sep-2019
Citation: Liu T, Zhang J, Gao G, Yang J & Marino A (2019) CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter. IEEE Transactions on Geoscience and Remote Sensing p. 24. https://doi.org/10.1109/tgrs.2019.2931353
Abstract: Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions (GΓDs). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust.
DOI Link: 10.1109/tgrs.2019.2931353
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. https://doi.org/10.1109/TGRS.2019.2931353
Notes: Output Status: Forthcoming/Available Online

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