Support Vector Machine applied to Underwater Target Classification

Underwater target classification is a complex task, due to the difficulty in identifying non-overlapping and stable feature set. It is required to choose the right algorithm, approach and technique, or the best combinations of approaches and techniques from a large set of options available in the literature for the specific problem. A binary classifier can tackle the problem by decomposing multiclass problem into binary class. This paper addresses the multiclass underwater classification problem using binary classifier Support Vector Machine (SVM). Three methods "all-againstall," "all-against-all Hierarchical," "one-againstall"("AVA","AVA-H","OVA") are tried out and performance using a particular feature derived from real data set is compared. A number of metrics are used to compare the performance. OVA gives a better performance with less computation compared t o other methods.