A genetic-optimized multi-angle normalized cross correlation SIFT for automatic remote sensing registration
Yingying Li, Qingjie Liu, Linhai Jing, Shuo Liu, Fengxian Miao · 2016
A new method of remote sensing image registration is proposed to reduce the adverse effects on the image registration caused by the rotation transform, based on genetic-optimized multi-angle normalized cross correlation (GMNCC) to get more matched scale invariant feature transform (SIFT) feature points. The GMNCC can detect the angle-offsets (AOs) of object images to reference image by determining the maximum of correlation coefficients between the two images with genetic algorithm, and complete the rotation offset correction. Then SIFT is used to extract the feature points and feature matching, which is subsequently refined by RANSAC to eliminate the false matched control points. Two Woldview-2 (WV2) images of Beijing Olympic Forest Park were used for testing the GMNCC-SIFT registration. GMNCC detects more accurate rotation angle-offsets than multi-angle normalized cross correlation (MANCC), reduces the detection process from 3000s to 2500s, and gives more matched points than simple SIFT to improve registration accuracy.