Precise co-registration of very high resolution optical images by registration-noise estimation

Very High Resolution (VHR) multitemporal images show a residual misalignment even after applying effective state of the art co-registration. This residual misalignment is caused by the dissimilarities of the acquisition circumstances such as off-nadir angle of the sensor, stability of the acquisition platform, structure of the considered scene, and so on. This paper aims at mitigating the residual misalignment of VHR multitemporal images to get a fine co-registration result. Here we propose to use Registration Noise (RN), which represents misaligned samples, for refining co-registration. After standard co-registration, a local analysis of RN pixels is fulfilled for extracting Control Points (CPs) and matching them according to the amount of the RN pixels. Matched CPs are employed for generating a deformation map to warp one image to the other image. Experiments carried out on both simulated and real multitemporal VHR images acquired by QuickBird sensors confirm the validity of the analysis and effectiveness of the proposed method.