  This paper focuses on potential accuracy of remote sensing images
registration. We investigate how this accuracy can be estimated without ground
truth available and used to improve registration quality of mono- and
multi-modal pair of images. At the local scale of image fragments, the
Cramer-Rao lower bound (CRLB) on registration error is estimated for each local
correspondence between coarsely registered pair of images. This CRLB is defined
by local image texture and noise properties. Opposite to the standard approach,
where registration accuracy is only evaluated at the output of the registration
process, such valuable information is used by us as an additional input
knowledge. It greatly helps detecting and discarding outliers and refining the
estimation of geometrical transformation model parameters. Based on these
ideas, a new area-based registration method called RAE (Registration with
Accuracy Estimation) is proposed. In addition to its ability to automatically
register very complex multimodal image pairs with high accuracy, the RAE method
provides registration accuracy at the global scale as covariance matrix of
estimation error of geometrical transformation model parameters or as
point-wise registration Standard Deviation. This accuracy does not depend on
any ground truth availability and characterizes each pair of registered images
individually. Thus, the RAE method can identify image areas for which a
predefined registration accuracy is guaranteed. The RAE method is proved
successful with reaching subpixel accuracy while registering eight complex
mono/multimodal and multitemporal image pairs including optical to optical,
optical to radar, optical to Digital Elevation Model (DEM) images and DEM to
radar cases. Other methods employed in comparisons fail to provide in a stable
manner accurate results on the same test cases.
