Adaptive stochastic gradient descent optimization in multi temporal satellite image registration

Abstract

Image registration of satellite imagery is challenging task as the image pair may have been captured by different sensors, view angles or at different times. Conventional satellite image registration tasks are achieved by feature based techniques which are to be identified and matched before estimating transforms and resample the moving or slave image to a fixed or master image. However, automatic image registration is very important requirement for voluminous data sets. Even if feature selection made automatic, even distribution and the density of feature points is always not ensured. A good alternative is to switch over to intensity based image registration techniques which are somewhat image content independent. We bring out details on an intensity based multi temporal satellite image registration task with metric, transform and optimization components based method. An optimization technique employed to find optimal transform parameters by maximizing the chosen similarity measure criteria, provides a robust image registration framework. Adaptive Stochastic Gradient Descent optimization (ASGD) for satellite image registration is a promising new technique, details of which are reported here.