Automatic Feature Points Based Registration Technique of Satellite Images

Feature points based image registration is a technique used in computer vision and image processing to align two or more images by identifying and matching distinctive feature points between them. The purpose is to establish a geometrical transformation between two images to be compared that spatially aligns each point from one image to its corresponding of the other image. Recently, local feature key-points such as scale invariant feature transform (SIFT), speeded-up robust features (SURF) and oriented FAST and rotated BRIEF (ORB) are widely used due to their inherent properties such as invariance, changes in illumination, and noise. This study provides an automated feature points based registration technique applied to satellite images. The registration task is solved by an iterative heuristic optimization method namely genetic algorithm (GA) to find the best rigid transformation parameters by minimizing the distance between the two feature point sets. An adaptive GA based on fitness sharing and elitism techniques is performed to enhance the capability of GA. A comparative study is conducted between different extraction feature techniques. Therefore, the results demonstrate the effectiveness of the SIFT algorithm compared to the other competing techniques for registering satellite images.