Proc. SPIE. 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII
KEYWORDS: Target detection, Receivers, Feature extraction, Image filtering, Chemical elements, Electronic filtering, Submerged target detection, Simulation of CCA and DLA aggregates, Neurons, Classification systems
In this paper, two different multi-aspect underwater target classification systems are evaluated based on their ability to correctly detect and classify mine-like objects. These methods are tested on a recently collected database that consists of sonar returns from various buried mine-like and non-mine-like objects in different operating and environmental conditions. In one approach, coherent features are extracted from the data using canonical correlation analysis (CCA) between two sonar pings. Classification is performed using a collaborative multi-aspect classifier (CMAC), which utilizes a group of collaborative decision-making agents capable of producing a high-confidence final decision based on these features. The second approach uses features generated by a multi-channel coherence analysis (MCA), which is an extension of CCA utilizing multiple sonar pings. The MCA features are then applied to a simple classifier. Results are presented in terms of correct classification rate and general detection and classification performance of each system in relation to the various operating and environmental conditions.
Proc. SPIE. 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI
KEYWORDS: Target detection, Metals, Composites, Receivers, Feature extraction, Detector development, Image filtering, Canonical correlation analysis, Simulation of CCA and DLA aggregates, Classification systems
Developing an effective detection and classification system for use with buried underwater objects is a challenging problem. In this paper, multichannel canonical correlation analysis (MCCA) is used for feature extraction from multiple sonar returns of buried underwater objects using data collected by the new generation Buried Object Scanning Sonar (BOSS) system. Comparisons are made between the classification results of features extracted by the proposed algorithm and those extracted by the two-channel canonical correlation analysis (CCA) algorithm on the SAX '04 data set. Extracted features are subsequently used in the development of classification systems able to differentiate between mine-like and non-mine-like objects. This study compares different feature extraction algorithms and classification schemes, and the results are presented in terms of classification rates and overall detection/classification performance. The results show that, for the SAX '04 data set, the features extracted via MCCA yield higher correct classification rates than feature extracted using CCA while simultaneously reducing structural complexity.