Conference Proceeding

Optimal polarimetric detection filter and its statistical tests for a ship detector

Details

Citation

Liu T, Dias RYCL, Yang J, Marino A & Gao G (2019) Optimal polarimetric detection filter and its statistical tests for a ship detector. In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Proceedings. 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28.07.2019-02.08.2019. Piscataway, NJ, USA: IEEE, pp. 3221-3224. https://doi.org/10.1109/IGARSS.2019.8900395

Abstract
Ship detection is one important task in radar remote sensing. Moreover, Polarimetry shows a valuable contribution to discriminate between targets and clutter. The performance of most polarimetric detectors depends on two important factors: target clutter ratio (TCR) and speckles (or standard deviation to mean ratio of clutter background). The polarimetric matched filter (PMF) is just to maximize the TCR, while the polarimetric whitening filter (PWF) only takes the speckle reduction into consideration. In this paper, the optimal polarimetric detection filter (OPDF) is put forward, which considers maximizing the ratio of TCR to speckle. The approximate expression of the probability density function (PDF) of the OPDF is derived in closed form, so are the probability of false alarm (PFA) and the probability of detection (PD) in Wishart distribution assumption. The threshold of the OPDF detection can be easily obtained in closed form or via the bisection method. Experiments via simulated data validate the correctness of our results. The OPDF detector gives the best performance in most environments, especially in low PFA case and in the case where the statistics of targets is not the ideal Wishart distribution.

Keywords
Polarimetric detection; Synthetic Aperture Radar; Target to Clutter Ratio; Speckle; Optimal Polarimetric Detection

StatusPublished
Publication date31/12/2019
Publication date online14/11/2019
URLhttp://hdl.handle.net/1893/31014
PublisherIEEE
Place of publicationPiscataway, NJ, USA
ISSN of series2153-7003
eISBN978-1-5386-9154-0
Conference2019 IEEE International Geoscience and Remote Sensing Symposium
Conference locationYokohama, Japan
Dates

People (1)

People

Dr Armando Marino
Dr Armando Marino

Senior Lecturer in Earth Observation, Biological and Environmental Sciences