Statistical Nonrecursive Spatial Filter For Processing Of Infrared Mosaic Sensor Images

Statistical nonrecursive spatial filters are used to process noisy infrared mosaic sensor images for background clutter suppression and target detection. It is assumed that the clutter can be modeled as an additive, spatially correlated noise and described by its auto-correlation function. The filter is designed to estimate the targets and also to suppress the clutter based on "a priori" knowledge of the statistical property of the noise. Square shaped spatial filters are studied. Signals of all pixels in the filter are linearly weighted and summed to estimate the signal at the center of the filter. The weighting coefficients are designed by a minimization of mean squared error procedure. Target detection is accomplished by thresholding after the spatial filtering. Furthermore, this filter can also be designed to accomplish discrimination of point and line targets simultaneously with the enhancement of target to clutter ratio. Filter results based on computer simulation are shown.