Use of morphology and the low Fourier frequencies in hierarchical fuzzy FLIR target segmentation and classification

A fuzzy hierarchical FLIR ATR is proposed which more closely models the fuzziness in the FLIR data and the human decision process than the traditional ATR methods. The target and its internal hot spots are segmented out from the background by use of an iterative volume based morphological contrast peak extraction routine. The segmented regions are then represented by a set of silhouettes for each segmented blob rather than just the one `best' silhouette. For the target or foundation segment, the primary recognition feature, silhouette shape, is captured by the low frequencies of the 2-D DFT of each member of the set. The hot spots are represented both by the shape features (DFT) and by positional features. The first level of this hierarchical classification system uses an Euclidean distance figure of merit for the foundation's silhouette to assign a fuzzy classification to the target. This initial guess is then adjusted based on the internal features.