Contourlet detection and feature extraction for automatic target recognition

This research presents a contourlet based detection and feature extraction method for underwater targets. The method operates on Side Scan Sonar (SSS) images and is designed to automatically detect and generate target features for classification. Kernel based classifiers are used to determine the be...

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Bibliographic Details
Main Authors: Wilbur, J.-E., McDonald, R.J., Stack, J.
Format: Conference Proceeding
Language:English
Subjects:
contourlet
directional filterbank
Feature extraction
Filter bank
Kernel
kernel matching pursuit
Pixel
relevance vector machine
Sea floor
Shape
Sonar detection
support vector machince
Target recognition
Underwater tracking
USA Councils
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Description
Summary:This research presents a contourlet based detection and feature extraction method for underwater targets. The method operates on Side Scan Sonar (SSS) images and is designed to automatically detect and generate target features for classification. Kernel based classifiers are used to determine the best boundary for separating targets and clutter. A statistically significant target data set is generated by embedding additional synthetic targets into SSS data collected during sea tests. Feature trade off studies show an improvement in classification results with the addition of directional based features.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2009.5346564