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Synthetic aperture radar ship detection using Haar-like features
journal contribution
posted on 2023-05-18, 23:53 authored by Schwegmann, CP, Kleynhans, W, Brian SalmonBrian SalmonThe detection of ships at sea is a complex task made more so by adverse weather conditions, lack of night visibility and large areas of concern. Synthetic Aperture Radar imagery with large swaths can provide the needed coverage at a reduced resolution. The development of ship detection methods that can effectively detect ships despite the reduced image resolution is an important area of research. A novel ship detection method is introduced that makes use of a standard Constant False Alarm Rate prescreening step followed by a cascade classifier ship discriminator. Ships are identified using Haar-like features using AdaBoost training on the classifier with an accuracy of 89.38% and false alarm rate of 1.47 x 10-8 across a large swath Sentinel-1 and RADARSAT-2 newly created SAR dataset.
History
Publication title
IEEE Geoscience and Remote Sensing LettersVolume
14Pagination
154-158ISSN
1545-598XDepartment/School
School of EngineeringPublisher
Institute of Electrical and Electronics EngineersPlace of publication
United States of AmericaRights statement
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