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Sediment Classification Using Side Scan SONAR
Anuja Pharate#1, Jyoti Rangole

Published in: Journal for Advance Research in Applied Sciences
Volume- 4, Issue-7, pp.380-384, Dec 2017
DPI :-> 16.10089.JARAS.2017.V4I7.380384.2263



Abstract
Accurate classification of seabed or riverbed is important in many more applications like dradging, study of marine biology, coastal engineering, hydrography etc. Numerous methods have already been proposed for seabed classification. In this paper, we presented a method to classify a given side scan SONAR images depending on type of sediment such as sand, mud, rock etc. In this study, we first employed discrete wavelet transform for extracting the features from side scan sonar images followed by applying principal component analysis (PCA) to reduce the dimensions of features. These reduced features then applied to support vector machine (SVM) for classification. In this study sediments are categorised into 6 different classes, SVM classifiers are suitable for classification into 2 classes. Here, strategy of one-against-all multiclass SVM is used to enhance generalization of SVM. The method is tested to a database that was readily available database of EdgeTech DF1000 side scan sonar image data, project REBENT, IFREMER (Location: France) is used. Database consists of total 240 images for 6 different classes. The result shows that DWT+PCA+SVM achieve best accurate classification results

Key-Words / Index Term
SONAR, Side Scan SONAR, discrete wavelet transform, principal component analysis, support vector machine.

How to cite this article
Anuja Pharate#1, Jyoti Rangole , “Sediment Classification Using Side Scan SONAR”, Journal for Advance Research in Applied Sciences, 4, Issue-7, pp.380-384, Dec 2017. DPI:16.10089.JARAS.V4.I7.2263