Robust automatic target recognition using a localized boundary representation

摘要

A localized classification approach which includes a localized target boundary representation, a set of local features to characterize parts of a target boundary, and a feature matching method is developed to classify highly degraded targets. Each pixel on the target boundary is represented by the perpendicular Euclidean distance between the boundary pixel and the chord connecting the end-points of a window centered on the boundary pixel. The resulting localized representation is quite robust with respect to noise and missing segments in the targets. A local feature is the segment of the localized representation between two points selected on the target boundary. The two points on the boundary may be specified or randomly selected. The reference features of a hypothesized target class are compared with segments of the localized representation of the test target to determine a dissimilarity measure between the hypothesized target class and the test target. Segments of the test target may also be matched with the reference localized contour sequences of the targets. The test target is assigned to a target class using a minimum mismatch rule. Results from a series of experiments conducted on three targets show that it is possible to classify targets experiencing high levels of noise and high percentages of missing segments in partial targets.