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

An image processing technique is described for the detection of mines in IR 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) projections 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× 28, which would require O(10) neural net weights, are processed by a network having O(10) weights. ROC curves are presented for mine detection in real and simulated imagery.