Overcoming the combined effect of geometric distortion and object change in image registration and conflation
Boris Kovalerchuk, Yuliya Kamatkova, Peter J. Doucette · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
A persistent problem with new unregistered geospatial data is geometric image distortion caused by different sensor/camera location. Often this distortion is modeled by means of arbitrary affine transformations. However in most of the real cases such geometric distortion is combined with other distortions caused by different image resolutions, different feature extraction techniques and others. Often images overlap only partially. Thus, the same objects on two images can differ significantly. The simple geometric distortion preserves one-to-one match between all points of the same object in the two images. In contrast when images are only partially overlapped or have different resolution there is no one-to-one point match. This paper explores theoretical and practical limits of building algorithms that are both robust and invariant at the same time to geometric distortions and change of image resolution. We provide two theorems, which state that such ideal algorithms are impossible in the proposed formalized framework. On the practical side we explored experimentally the ways to mitigate these theoretical limitations. Effective point placement, feature interpolation, and super-feature construction methods are developed that provide good registration/conflation results for the mages of very different resolutions.