22 July 1997 Acoustic/magnetic fusion system architecture variants and their classification performance
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Proceedings Volume 3079, Detection and Remediation Technologies for Mines and Minelike Targets II; (1997); doi: 10.1117/12.280849
Event: AeroSense '97, 1997, Orlando, FL, United States
Abstract
Research in FY-95 first addressed the problem of combining high-frequency (HF) side-scan sonar imagery, low-frequency (LF) side-scan Sonar imagery, and magnetic gradiometer data in order to detect/classify undersea mines. The first approach developed, termed the "Blob-Pair" based acoustic/magnetic (AM) Fusion system architecture, implicitly assumed that a target manifests itself in both HF,LF imagery, and was based on the fusion of single-sensor derived neural network classifier discriminants at a collection of three "decision" nodes, identified with magnetic (M), HF/LF, and HF/LF/M - data fusion cases, respectively. In order, to remove the restrictive assumption of a target manifesting in both }IF,LF data, the "Generalized" AM Fusion Architecture was developed, with a total of 7 "decision" nodes, identified with M, HF, HF/LF, LF, HF/M, HF/LFIM, and LF/M data fusion cases, respectively. However, the "Generalized" AM-Fusion architecture was found empirically to have significantly increased number of false alarms, relative to the "Blob-Pair" based system. Hence, through two-additional AM-Fusion architecture varaints, involving first the use of Classification Token "Post-Processing", and then both Token "Post-Processing" and decision node statistic modification, the performance "gap" between "Blob-Pair" and "Generalized" AM-Fusion Architecture performance was closed.
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Martin G. Bello, "Acoustic/magnetic fusion system architecture variants and their classification performance", Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); doi: 10.1117/12.280849; https://doi.org/10.1117/12.280849
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KEYWORDS
Data fusion

Classification systems

Image fusion

Magnetism

Mining

Neural networks

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