Paper
29 April 2008 Target detection from dual disparate sonar platforms using canonical correlations
Author Affiliations +
Abstract
In this paper a new coherence-based feature extraction method for sonar imagery generated from two disparate sonar systems is developed. Canonical correlation analysis (CCA) is employed to identify coherent information from co-registered regions of interest (ROI's) that contain target activities, while at the same time extract useful coherent features from both images. The extracted features can be used for simultaneous detection and classification of target and non-target objects in the sonar images. In this study, a side-scan sonar that provides high resolution images with good target definition and a broadband sonar that generates low resolution images, but with reduced background clutter. The optimum Neyman-Pearson detector will be presented and then extended to the dual sensor platform scenarios. Test results of the proposed methods on a dual sonar imagery data set provided by the Naval Surface Warfare Center (NSWC) Panama City, FL will be presented. This database contains co-registered pair of images over the same target field with varying degree of detection difficulty and bottom clutter. The effectiveness of CCA as the optimum detection tool is demonstrated in terms of probability of detection and false alarm rate.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mahmood R. Azimi-Sadjdadi and J. Derek Tucker "Target detection from dual disparate sonar platforms using canonical correlations", Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 69530J (29 April 2008); https://doi.org/10.1117/12.776465
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Sensors

Target detection

Simulation of CCA and DLA aggregates

Feature extraction

Image resolution

Image classification

Radon

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