Optimal tuning of the contour analysis method to recognize aircraft on remote sensing imagery

In this paper, we describe the experimental results of aircraft recognition on optical remote sensing imagery using the theory of contour analysis. We propose the a method to calculate optimal values of the contour’s items quantity and the classification threshold through measuring within- and between-class distances for all possible training set instances combinations with followed by detection and minimization of the type I and II errors. We discuss the construction of contours’ similarity measures combining the principles of finding the most appropriate reference instance and calculating the average value for the whole class. It is shown that the proposed parameters' tuning method and the similarity function make contour analysis capable to train on compact non-uniform datasets and to recognize aircraft on the noisy and less detailed images.