Multi-Resolution SAR and Optical Remote Sensing Image Registration Methods: A Review, Datasets, and Future Perspectives
Journal:
arXiv
Published Date:
Feb 3, 2025
Abstract
Synthetic Aperture Radar (SAR) and optical image registration is essential
for remote sensing data fusion, with applications in military reconnaissance,
environmental monitoring, and disaster management. However, challenges arise
from differences in imaging mechanisms, geometric distortions, and radiometric
properties between SAR and optical images. As image resolution increases, fine
SAR textures become more significant, leading to alignment issues and 3D
spatial discrepancies. Two major gaps exist: the lack of a publicly available
multi-resolution, multi-scene registration dataset and the absence of
systematic analysis of current methods. To address this, the MultiResSAR
dataset was created, containing over 10k pairs of multi-source,
multi-resolution, and multi-scene SAR and optical images. Sixteen
state-of-the-art algorithms were tested. Results show no algorithm achieves
100% success, and performance decreases as resolution increases, with most
failing on sub-meter data. XoFTR performs best among deep learning methods
(40.58%), while RIFT performs best among traditional methods (66.51%). Future
research should focus on noise suppression, 3D geometric fusion, cross-view
transformation modeling, and deep learning optimization for robust registration
of high-resolution SAR and optical images. The dataset is available at
https://github.com/betterlll/Multi-Resolution-SAR-dataset-.
