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Synthetic aperture radar ship detection using Haar-like features

Citation

Schwegmann, CP and Kleynhans, W and Salmon, BP, Synthetic aperture radar ship detection using Haar-like features, IEEE Geoscience and Remote Sensing Letters, 14, (2) pp. 154-158. ISSN 1545-598X (2017) [Refereed Article]

Copyright Statement

2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.

DOI: doi:10.1109/LGRS.2016.2631638

Abstract

The 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.

Item Details

Item Type:Refereed Article
Keywords:synthetic aperture radar, image processing, pattern recognition, marine technology
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Signal Processing
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Engineering
Author:Salmon, BP (Dr Brian Salmon)
ID Code:112737
Year Published:2017
Web of Science® Times Cited:3
Deposited By:Engineering
Deposited On:2016-11-25
Last Modified:2017-11-09
Downloads:0

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