Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

Brian A. Baertlein; Wen-Jiao Liao

doi:10.1117/12.396244

22 August 2000 Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

Brian A. Baertlein, Wen-Jiao Liao

Proceedings Volume 4038, Detection and Remediation Technologies for Mines and Minelike Targets V; (2000) https://doi.org/10.1117/12.396244
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(10⁹) neural net weights, are processed by a network having 0(10²) weights. ROC curves are presented for mine detection in real and simulated imagery.

Citation Download Citation

Brian A. Baertlein and Wen-Jiao Liao "Wavelet-based higher-order neural networks for mine detection in thermal IR imagery", Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); https://doi.org/10.1117/12.396244

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