Clutter Rejection in FLIR Imagery Using Spatially-Varying Adaptive Filtering

This paper introduces an adaptive method for rejecting clutter in Forward-Looking Infra-Red (FLIR) imagery. In this approach, a spatially-varying two-dimensional adaptive filtering method is developed to identify a metric distance (error energy) between a test scene and a library of reference man-made targets (trucks, tanks, etc.). The function of the 2D spatially-varying adaptive filter is to compensate for: a) variations of Image Point Response (IPR) of a FLIR sensor; b) variations of heat distribution on the test and reference targets; c) sub-pixel shifts in the relative coordinates of these targets; and d) subtle scaling and rotations. A statistic is constructed that is the least square energy of the error between the test target and its projection into each member of the library of reference target chips based on the above-mentioned spatially-varying 2D adaptive filtering; this database is then used to identify the test chip as a man-made target or clutter. Results with FLIR data of scenes composed of trucks, tanks, and APCs at various angles in different types of clutter environment will be provided.