Article

PolSAR Ship Detection Based on Neighborhood Polarimetric Covariance Matrix

Details

Citation

Liu T, Yang Z, Marino A, Gao G & Yang J (2021) PolSAR Ship Detection Based on Neighborhood Polarimetric Covariance Matrix. IEEE Transactions on Geoscience and Remote Sensing, 59 (6), pp. 4874-4887. https://doi.org/10.1109/tgrs.2020.3022181

Abstract
The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms.

Keywords
Marine vehicles; Covariance matrices; Detectors; Correlation; Synthetic aperture radar; Clutter; Scattering

Journal
IEEE Transactions on Geoscience and Remote Sensing: Volume 59, Issue 6

StatusPublished
FundersNational Natural Science Foundation of China, National Natural Science Foundation of China, National Natural Science Foundation of China, Field Foundation of Illinois, Fundamental Research Funds for the Central Universities and Key Research Plan of Hunan Province
Publication date30/06/2021
Publication date online24/09/2020
Date accepted by journal04/09/2020
URLhttp://hdl.handle.net/1893/31755
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN0196-2892
eISSN1558-0644

People (1)

Dr Armando Marino

Dr Armando Marino

Associate Professor, Biological and Environmental Sciences