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.
Aiming at the registration of optical remote sensing images, an algorithm based on strong edge region is proposed. First,
the strong edge regions are extracted. Then combines with the regions' moment invariants and RANSAC method, it can
obtain an accurate match of the strong edge regions. Utilizing the centroids of the matching regions as control points in
the affine geometric distortion, an automatic registration is performed. A large number of experiments are fulfilled with
SPOT and Quickbird satellite images and good results are obtained.