A Study on Detection and Classification of Underwater Mines using Neural Networks
S. N. Geethalakshmi1, P. Subashini2, S. Ramya3
1Dr. S. N. Geethalakshmi, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.
2Dr. P. Subashini, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.
3Ms. S. Ramya, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.
Manuscript received on October 04, 2011. | Revised Manuscript received on October 21, 2011. | Manuscript published on November 05, 2011. | PP: 150-157 | Volume-1 Issue-5, November 2011. | Retrieval Number: E0173091511/2011©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mine detection and classification using side scan sonar imagery is a challenging problem. As opposed to the majority of techniques, several Neural-network-based methods for the detection and classification of mines and mine like objects have been proposed. Detection and classification of underwater objects in sonar imagery is a complicated problem, due to various factors such as variations in operating and environmental conditions, presence of spatially varying clutter, variations in target shapes, compositions and orientation. Moreover, bottom features such as coral reefs, sand formations, and the attenuation of the sonar signal in the water column can totally obscure a mine-like object. Side scan sonar is a proven tool for detection of underwater objects. In order to overcome such complicated problems detection and classification system is needed. This method is able to extrapolate beyond the training data and successfully classify mine-like objects (MLOs). Five basic components of detection and classification techniques are considered namely data preprocessing, segmentation, feature extraction, detection and classification. In this paper nearly fifteen research papers of neural network techniques have been reviewed.
Keywords: Segmentation, Feature extraction, Side scans sonar, Image classification, Underwater mine detection, Neural networks.