Full system modelling for hyperspectral target detection and identification

The problem of the automatic detection and identification of military vehicles in hyperspectral imagery has many possible solutions. The availability and utility of library spectra and the ability to atmospherically correct image data has great influence on the choice of approach. This paper concentrates on providing a robust solution in the event that library spectra are unavailable or unreliable due to differing atmospheric conditions between the data and reference. The development of a number of techniques for the detection and identification of unknown objects in a scene has continued apace over the past few years. A number of these techniques have been integrated into a "Full System Model" (FSM) to provide an automatic and robust system drawing upon the advantages of each. The FSM makes use of novel anomaly detectors and spatial processing to extract objects of interest in the scene which are then identified by a pre-trained classifier, typically a multi-class support vector machine. From this point onwards adaptive feedback is used to control the processing of the system. Stages of the processing chain may be augmented by spectral matching and linear unmixing algorithms in an effort to achieve optimum results depending upon the type of data. The Full System Model is described and the boost in performance over each individual stage is demonstrated and discussed.