Aircraft type recognition based on convex hull features and SVM

Abstract

Most current algorithms of aircraft type recognition are based on the binary images which are obtained by utilizing the technology of image segmentation. Thus the effect of image segmentation will influence the sequent classification to a great extent. Moreover, image segmentation in complex background remains a challenging research area. In our work, we propose a novel aircraft type recognition algorithm based on the aircrafts' convex hull features and Support Vector Machine (SVM). We first obtain the aircrafts' external contours while removing background. And then, we compute the planar convex hulls of the external contours. Based on the convex hulls, we combine the characteristics unique to the aircraft object, to introduce an extracting method of major symmetry axle and corresponding characters. Finally, we select the SVM which has high generalization capabilities and high performance in tackling small sample size in the pattern classification task to perform the classification. Experiment results show that the convex hull feature of aircraft object is approximately invariant, and can successfully eliminate the need to segment the object region from the complex background. The aircraft type recognition is efficient and feasible, and especially applicable for raw gray images.