Satellite image matching and registration using affine transformation and hybrid feature descriptors

Abstract

Image registration (IR) is an image processing technique to determine geometrical transformation that gives the most accurate match between reference and floating images. In existing IR work, the inliers ratio and outlier's ratios are equal. This reduces the image registration accuracy. So, to avoid the outlier's ratio and increase the inliers ratio, hybrid invariant local features descriptor is proposed. This feature descriptor consists of binary-robust-invariant-scalable-key point (BRISK), speed-up-robust-feature (SURF), and feature from accelerated segment test feature (FAST). The hybrid feature descriptor extracts the relevant features from the image. Then, the feature matching step finds the correct correspondences from the two sets of features. Also, affine transformation avoids the false feature matching points. An experimental analysis shows the inliers ratio and repeatability evaluation metrics performance of individual feature descriptor, combined feature descriptor and proposed feature descriptor. The proposed hybrid feature descriptor achieves inliers ratio of 1.913 and repeatability is 0.121.