Object detection by radial basis neural network filtering of spectral data

Abstract

An object recognition technique has been developed that allows the rapid screening of multispectral images for objects with known spectral signatures. The technique is based on the configuration of a radial basis neural network (RBN) that is specific for a particular object spectral signature or series of object spectral signatures. The method has been used to identify features in CASI-2 and HYDICE images with results comparable to conventional spatial object recognition techniques with a significant reduction in processing time. Radial basis neural networks have several advantages over the more common backpropagation neural networks, including better selectivity and faster training, resulting in a significant reduction in overall image processing time and greater accuracy.