A comparison of geometric features for object classification in aerial imagery

摘要

This paper examines the use of three feature sets for object classification in aerial imagery. The first feature set is based on affine invariant functions of the central moments computed on the objects within the image. The second feature set employed Zernike moment invariants and the third feature set utilized affien invariant functions of the central moments that are computed over a spline fit to the object bounadary. The initial object locations were obtained using either a region of interest identification process based on low-level image processing techniques or a hand extraction process. A single nearest neighbor, k-nearest eighbors, and a weighted k-nearest neighbors classifier were employed to evaluate the utility of the various feature sets for both the hand extracted and rregion if interest identified objects. The performance of the full system is characterized via probability of detection and probability of false alarm.