Several of the sensor technologies employed for producing hyperspectral images make use of dispersion across an array to generate the spectral content along a 'line' in the scene and then use scanning to build up the other spatial dimension of the image. Infrared staring arrays rarely achieve 100% fully functioning pixels. In single-band imaging applications 'dead' elements do not cause a problem because simple spatial averaging of neighboring pixels is possible (assuming that a pixel is similar in intensity to its neighbors is a reasonably good approximation). However, when the array is used as described above to produce a spectral image, dead elements result in missing spatial and spectral information. This paper investigates the use of several novel techniques to replace this missing information and assesses them against image data of different spatial and spectral resolutions with the aim of recommending the best technique to use based on the sensor specification. These techniques are also benchmarked against naive spatial averaging.
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.