Co-Registration of LISS-4 Multispectral Band Data Using Mutual Information-Based Stochastic Gradient Descent Optimization

We propose a solution for automatic co-registration of LISS-4 MX radiometrically conditioned multi-spectral images issue by considering an optimization problem in which mutual information-based approach is used. Co-registration of multi-spectral images from the same sensor may also be a tough problem to tackle, whenthe payload imaging geometry is complex. The multi-spectral images acquired by ISRO Resources at-1/2 LISS-4 MX class of sensors pose such problems and demand an automatic registration solution for system corrected product generation to cater to user needs. Optical remote sensing image registration is assisted by image geo-referencing or navigation information along with components such as feature detection, matching, correspondence, and resampling the input image to the reference geometry. Intensity-based methods employ an iterative registration framework,where similarity metric based image matching and correspondence is refined to find out optimum transform parameters. We could successfully employ mutual information-based adaptive stochastic gradient descent optimization algorithm to do sub-pixel level satellite image registration tasks by a careful choice of parameters and models related to metric, transform, optimizer, and interpolator in a robust image registration framework which is automatic for different terrain data. The performance is also compared to a recent scale invariant feature transform (SIFT)-based registration method.