Multimodal Remote Sensing Image Registration with Accuracy Estimation at Local and Global Scales
M.L. Uss, B. Vozel, V.V. Lukin, K. Chehdi

TL;DR
This paper introduces RAE, a novel image registration method that estimates accuracy without ground truth, improving registration quality for complex multimodal remote sensing images at local and global scales.
Contribution
The paper proposes RAE, a new registration method that uses local CRLB-based accuracy estimation to enhance registration and provide reliable accuracy metrics without ground truth.
Findings
RAE achieves subpixel accuracy on complex multimodal images.
It provides reliable local and global registration accuracy estimates.
Compared to other methods, RAE demonstrates superior stability and precision.
Abstract
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…
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