Intelligent Target Detection in Hyperspectral Imagery

Many applications that use hyperspectral imagery focus on detection and recognition of targets that occupy a portion of a hyperspectral pixel. We address the problem of sub-pixel target detection by evaluating individual pixels belonging to a hyperspectral image scene. We begin by clustering each pixel into one of n classes based on the minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Associated with each cluster is a set of linear filters specifically designed to separate signatures derived from a target embedded in a background pixel from other typical signatures belonging to that cluster. The filters are found using directed principal component analysis which maximally separates the two groups. Each pixel is projected on this set of filters and the result is fed into a trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for generating training and testing data, describe our modified clustering algorithm, explain how the linear filters are designed, and provide details on the neural network classifier. Evaluation of the overall algorithm demonstrates that for pixels with embedded targets taking up no more than 10% of the area, our detection rates approach 99.9% with a false positive rate of less than