Noise-Robust Multiscale Structural Feature for Optical and SAR Image Matching

Optical and synthetic aperture radar (SAR) image matching is fundamental to numerous remote sensing applications. Although phase congruency-based methods have been widely adopted for this task, the structural features they extract remain inherently sensitive to speckle noise, which significantly degrades matching performance. To address this limitation, we propose a novel noise-robust multiscale structural feature (NMSF) method. Unlike existing relative total variation (RTV)-based approaches that modify the RTV model or rely on phase congruency, our framework employs the standard RTV model with a principled exponential scaling scheme to construct multiscale structural maps (MSM), effectively capturing salient structural information across multiple scales while suppressing noise and mitigating modality discrepancies. Building upon the MSM, we develop an MSM-ORB detector with non-maximal suppression to extract reliable and well-distributed keypoints. We further introduce a multilayer features of oriented gradient (MFOG) descriptor with a square grid nested architecture, which fundamentally differs from the log-polar structures used in prior methods and achieves multiscale integration without dimensional inflation. Extensive experiments on publicly available datasets demonstrate that NMSF consistently outperforms state-of-the-art approaches, delivering superior robustness and accuracy under nonlinear intensity variations, geometric distortions, and severe speckle noise.