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Automatic Target Detection in Swath-Sonar Images, Using Texture Based Unsupervised Classification and Blind Source Deconvolution
1  Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras, 265 00 Rion, Greece

Published: 23 June 2015 by MDPI in 1st International Electronic Conference on Remote Sensing session Applications

Although in the field of Underwater Acoustic Imaging a large number Automatic Target Detection (ATD) systems have long been developed and their bibliography has been classified, the present study aims to test the potentiality of using common and widely used feature based image classification approaches against small targets detection in SideScan Sonar (SSS) images by combining them with powerful data mining techniques. Conventional Acoustic Classification Systems (ACS) start by extracting numerous texture descriptors from distinct image neighbourhoods throughout the image and forming large Feature Vectors (FVs). In view of the FVs’ high dimensionality, prior to unsupervised classification, a component analysis technique, usually Principal Component Analysis (PCA), is performed to decompose them into a few un-correlated features that explain the majority of the image’s variance. However, small targets belong to subordinary image information and do not contribute significantly to the total information variance of the SSS image. Furthermore targets tend to be independent image characteristics rather than un-correlated ones. In this study, a newly available technique, called Independent Component Analysis (ICA), that decomposes the FVs into independent sources, is tested against its ability to separate SSS images into targets and background and lead to accurate target classification.

The proposed methodological scheme consists of the following stages: 1) windowed feature extraction, 2) ICA  decomposition, 3) selection of certain components that enhance potential targets through a maximum curtosis criterion, 4) decision of the  number of classes that the selected components need to be clustered into so that they are optimally separated in the Euclidean space through validation indices utilization, 5) unsupervised classification and 6) selection of the class or classes that most possibly correspond to areas containing potential targets via a minimum area definition. The above stages are included in the SonarClass Matlab ACS. The classification precision of the proposed system was assessed using a SSS dataset from Igoumenitsa Harbour, Greece, including more than 85 ground truthed man made targets. The classification accuracy of the proposed system was estimated as Pc=tp/(tp+fp), where tp is the number of true positive (expected) and fp the number of false positive (unexpected) predictions, and was compared to the accuracy of following conventional ACS procedures. The method exhibited unquestionable superiority indicating that ICA may worth further attention by ATD system researchers and developers.

Keywords: side-scan sonar, target detection, acoustic classification systems, texture, SonarClass, principal component analysis, independent component analysis