Pattern recognition in hyperspectral imagery using one dimensional maximum average correlation height filter and Mahalanobis distance

Abstract

Pattern recognition in hyperspectral imagery is a challenging task as the objects occupy only a few pixels or less. The presence of noise can make detection more complicated as spectral signature of pixels can change due to noise. In this paper a technique is proposed for detection in hyperspectral imagery using one dimensional maximum average correlation height (MACH) filter. MACH filter is a type of matched spatial training filter which is widely used for spatial aperture radar (SAR), laser radar (LADAR), forward looking infrared (FLIR) and other class of two-dimensional imageries to train and detect objects. For hyperspectral case a modified one-dimensional MACH filter is proposed which uses likely variations of a given ideal spectral signature for training. Each pixel vector of the data cube is then compared with the detection filter using Mahalanobis distance. Based on Mahalanobis distance between the trained filter and the pixels of the imagery, two classes are formed called the background class which does not contain a desired object and the object class which does contain the desired object. By applying threshold boundary, a decision is then made whether a given pixel belongs to the background class or object class. The simulation results using real life hyperspectral imagery show that the proposed technique can detect and classify the desired objects with a higher rate of efficiency even for very small and scattered objects.