Iterative Scale-Invariant Feature Transform for Remote Sensing Image Registration

Due to significant geometric distortions and illumination differences, developing techniques for high precision robust multisource remote sensing image registration poses a great challenge. This article presents an iterative approach, called scale-invariant feature transform (ISIFT) images, which extends the traditional (SIFT)-based system close-feedback SIFT that includes rectification feedback loop update rectified parameters in manner. Its key idea uses consistent point sets obtained by maximum similarity calculate new alignment rectify current sensed resulting is then fed back replace as reimplement next iteration. The same process repeated iteratively until automatic stopping rule satisfied. To evaluate performance of ISIFT, both simulated real images are used experiments validation ISIFT. In addition, several data particularly designed conduct comparative study analysis with existing state-of-the-art methods. Furthermore, different rotation also performed verify adaptability ISIFT under distortions. experimental results demonstrate improves produces better accuracy than SIFT-based methods