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Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: The Polarimetric Detection Optimization Filter and Its Statistical Test for Ship Detection
Author(s): Liu, Tao
Jiang, Yanni
Marino, Armando
Gao, Gui
Yang, Jian
Keywords: Optimal polarimetric detection (OPD)
polarimetric matched filter (PMF)
polarimetric whitening filter (PWF)
probability density function (PDF)
synthetic aperture radar (SAR)
constant false alarm rate (CFAR)
Issue Date: 15-Feb-2021
Date Deposited: 5-Mar-2021
Citation: Liu T, Jiang Y, Marino A, Gao G & Yang J (2021) The Polarimetric Detection Optimization Filter and Its Statistical Test for Ship Detection. IEEE Transactions on Geoscience and Remote Sensing.
Abstract: Ship detection via synthetic aperture radar (SAR) has been demonstrated to be very useful as polarimetric information helps discriminate between targets and sea clutter. Among the available polarimetric detectors, optimal polarimetric detection (OPD) theoretically provides the best detection performance under the assumption that the fully developed speckle hypothesis stands. This study proposes a polarimetric detection optimization filter (PDOF). The target clutter ratio (TCR) over the speckle variation was maximized using a matrix transform to derive the PDOF. The objective function based on a matrix transform instead of a vector transform is optimized to obtain synthetic effects by combining a polarimetric whitening filter (PWF) and a polarimetric matched filter (PMF). Subspace form of the PDOF (SPDOF) is also proposed, which gives performance comparable to the PDOF. Assuming a Wishart distribution, the exact and approximate expressions of the closed-form probability density function (PDF) of the PDOF are derived. The probability of false alarm (PFA) was derived in a closed-form expression, which allows obtaining the PDOF threshold analytically. Moreover, the gamma model is extended to a generalized gamma distribution (GΓD) to adapt complicated resolutions and sea states. Experiments with simulated and real data validate the correctness and effectiveness of the results. The PDOF detector achieves the best performance in most virtual and real-world environments, especially in cases where the target statistics and clutter are not Wishart-distributed.
DOI Link: 10.1109/tgrs.2021.3055801
Rights: © 2021 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.
Notes: Output Status: Forthcoming/Available Online

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