Abstract
This paper presents an algorithm for constructing a ternary phase-amplitude synthetic discriminant function filter tree for classifying an unknown image. The tree can classify an image in 2(DOT)Log<SUB>2</SUB>(N) filter applications, where N is the number of training set images used to construct the tree. An example tree is constructed to demonstrate the concept. The example tree contains 10 M60-A1 images and 10 T62 images from 0 degree(s) - 90 degree(s) orientation. The resulting tree is applied to 162 test images of M60 and T62 tanks that were not included in the training set. The tree was 100% accurate in classifying each test image as the correct type of tank, and was 94% accurate in identifying the training set image that each test image was closest to in orientation.
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