On the statistical examination of prevailing ship detection methodologies for space-based Synthetic Aperture Radar imagery – Improved capabilities through rigourous mathematical treatment and a novel sea clutter model

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Authors
  1. Gierull, C.
Corporate Authors
Defence Research and Development Canada, Ottawa Research Centre, Ottawa ON (CAN)
Abstract
The aim of this Scientific Report is threefold. First, based on a new rigorous analysis, a commonly adapted methodology for maritime surveillance with space-based SAR has been reviewed. This methodology has been used for the definition of requirements and specifications on vessel detection capabilities for current and future space-based Synthetic Aperture Radar (SAR) missions. The review includes deficiencies regarding the commonly applied sea clutter models, simplifying statistical assumptions as well as the description of the potential impact on previously drawn conclusions. Based on the impact, the performance of the classic model is demonstrated when computed properly and is compared against measured RADARSAT-2 data. Second, it is shown that the commonly applied K-distribution to model the SAR image amplitude as the basis of vessel detection in heterogeneous sea clutter may in practical cases be deficient for space-based systems and geometries. The report proposes a novel statistical model to describe the data more accurately compared to the classic K-statistics especially in challenging environments. This new sea clutter model stands out through its numerical simplicity, enhancing robustness and reducing the computational complexity considerably. Third, the report provides an analysis on the attainable performance of Doppler sublook or subband cross-correlation techniques. Literature from the last two decades claims that the techniques are capable of improving the detect

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Keywords
RADARSAT-2;space-based SAR;vessel detection
Report Number
DRDC-RDDC-2017-R024 — Scientific Report
Date of publication
01 Feb 2017
Number of Pages
97
DSTKIM No
CA045036
CANDIS No
805416
Format(s):
Electronic Document(PDF)

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