Automatic Target Recognition in Cluttered Visible Imagery

Abstract

Detection of a target from among similar/dissimilar targets in a cluttered image is vital in automatic target recognition. A MACH filter is used for the target detection in cluttered imagery. The cluttered images are correlated using the filter formulated from training images. The target is expected to be detected in the first highest peak. MACH was assumed as an optimum filter for target detection, but results proved that the accuracy is low. So, a new approach is identified for design of an optimum filter. The top ten peaks are identified as Regions of Interest (ROI's) and these regions are correlated with all the training images using matched filter in the second stage. To further increase the accuracy, edge enhancement of the training set and input images using Sobel operator is done. Results proved that these new approaches greatly improved the accuracy. Using matched filter in the second stage after MACH gives us an advantage of identifying the orientation of the target as well.