Sea mine detection and classification using side-looking sonar

John C. Hyland; Gerald J. Dobeck

doi:10.1117/12.211341

20 June 1995 Sea mine detection and classification using side-looking sonar

John C. Hyland, Gerald J. Dobeck

Proceedings Volume 2496, Detection Technologies for Mines and Minelike Targets; (1995) https://doi.org/10.1117/12.211341
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Coastal Systems Station has developed an approach for automatic mine detection and classification. The Detection Density ACF Approach was created by integrating the adaptive clutter filter (ACF) developed by Martin Marietta, the specification of target signature suggested by Loral Federal Systems, and the Attracted-Based Neural Network developed at NSWC Coastal Systems Station with a detection density target recognition criterion. The Detection Density ACF Approach consists of eight steps: image normalization, ACF, selecting the largest ACF output pixels, convolving the selected pixels with a minesize rectangular window, applying a Bayesian decision rule to detect minelike pixels, grouping the minelike pixels into objects, extracting object features, and classifying objects as either a mine or a nonmine with a neural network. When trained on features extracted from 30 sonar images and tested against another 30 images, this approach demonstrates very good performance: probability of detection and classification (p_dp_c) of 0.84 with a false alarm rate of 1.4 false calls per image. A performance analysis study shows that the detection density ACF approach performs very well and significantly reduces the false alarm rate.

Citation Download Citation

John C. Hyland and Gerald J. Dobeck "Sea mine detection and classification using side-looking sonar", Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); https://doi.org/10.1117/12.211341

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