Pattern recognition in hyperspectral imagery using Gaussian filter with post-processing

Abstract

Pattern recognition in hyperspectral imagery often suffers from a number of limitations, which includes computation complexity, false alarms and missing targets. The major reason behind these problems is that the spectra obtained by hyperspectral sensors do not produce a deterministic signature, because the spectra observed from samples of the same material may vary due to variations in the material surface, atmospheric conditions and other related reasons. In addition, the presence of noise in the input scene may complicate the situation further. Therefore, the main objective of pattern recognition in hyperspectral imagery is to maximize the probability of detection and at the same time minimize the probability of generating false alarms. Though several detection algorithms have been proposed in the literature, but most of them are observed to be inefficient in meeting the objective requirement mentioned above. This paper presents a novel detection algorithm which is fast and simple in architecture. The algorithm involves a Gaussian filter to process the target signature as well as the unknown signature from the input scene. A post-processing step is also included after performing correlation to detect the target pixels. Computer simulation results show that the algorithm can successfully detect all the targets present in the input scene without any significant false alarm.