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Remote Sens. 2016, 8(5), 373; doi:10.3390/rs8050373

An Automatic Target Detection Algorithm for Swath Sonar Backscatter Imagery, Using Image Texture and Independent Component Analysis

Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras, Rion 26500, Greece
These authors contributed equally to this work.
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Academic Editors: Ioannis Gitas, Arko Lucieer and Prasad S. Thenkabail
Received: 1 December 2015 / Revised: 22 April 2016 / Accepted: 27 April 2016 / Published: 29 April 2016
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Abstract

In the present paper, a methodological scheme, bringing together common Acoustic Seabed Classification (ASC) systems and a powerful data decomposition approach, called Independent Component Analysis (ICA), is demonstrated regarding its suitability for detecting small targets in Side Scan Sonar imagery. Traditional ASC systems extract numerous texture descriptors, leading to a large feature vector, the dimensionality of which is reduced by means of data decomposition techniques, usually Principal Component Analysis (PCA), prior to classification. However, in the target detection issue, data decomposition should point towards finding components that represent sub-ordinary image information (i.e., small targets) rather than a dominant one. ICA has long been proved to be suitable for separating targets from a background, and this study represents a novel exhibition of its applicability to Side Scan Sonar (SSS) images. The present study attempts to build a fully automated target detection approach that combines image based feature extraction, ICA, and unsupervised classification. The suitability of the proposed approach has been demonstrated using an SSS data-set containing more than 70 manmade targets, most of them metallic, validated through a marine magnetic survey or ground truthing inspection. The method exhibited very good performance as it was able to detect more than 77% of the targets and it produced less than seven false alarms per km2. Moreover, it was compared to cases where, in the exact same methodological scheme, no decomposition technique is used, or PCA is employed instead of ICA, achieving the highest detection rate, but, more importantly, producing more than six times less false alarms, thus proving that ICA successfully manages to maximize target to background separation. View Full-Text
Keywords: target detection; image texture; swath sonar; independent component analysis; principal component analysis; seabed classification; SonarClass target detection; image texture; swath sonar; independent component analysis; principal component analysis; seabed classification; SonarClass
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Fakiris, E.; Papatheodorou, G.; Geraga, M.; Ferentinos, G. An Automatic Target Detection Algorithm for Swath Sonar Backscatter Imagery, Using Image Texture and Independent Component Analysis. Remote Sens. 2016, 8, 373.

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