Adaptive TerraSAR-X Image Registration (AIR) Using Spatial Fisher Kernel Framework

TerraSAR-X image registration is a forerunner for remote sensing application like target detection, which need accurate spatial transformation between the real time sensed image and the reference off-line image. It is observed that the outcome of registration of two TerraSAR images even when acquired from the same sensor is unpredictable with all the parameters of the feature extraction, matching and transformation algorithm are fixed. Hence we have approached the problem by trying to predict if the given TerraSAR-X images that can be registered without actually registering them. The proposed adaptive image registration (AIR) approach incorporates a classifier into the standard pipeline of feature based image registration. The attributes for the classifier model are derived from fusing the spatial parameters of the feature detector with the descriptor vector in Fisher kernel framework. We have demonstrated that the proposed AIR approach saves the time of feature matching and transformation estimation for SAR images which cannot be registered.