Optical and SAR Images Automatic Registration Based on Anisotropic Diffusion Coefficient Feature Descriptors

The presence of disparities in radiation characteristics and diverse noise interferences is ubiquitous in multisource remote imagery (MSRI), which is captured across various platforms, at different times, and from multiple angles. Unfortunately, these problems significantly hinder the performance of gradient-based MSRI registration algorithms. To mitigate these challenges, we introduce a novel methodology for deriving feature descriptors, leveraging the mapped anisotropic diffusion coefficient (ADC) as the cornerstone. Initially, the images are processed by a series of anisotropic diffusion filters to construct an anisotropic scale space (ASS) and facilitate the subsequent computation of the mapped ADC. Subsequently, in the Harris scale space, rooted in the ASS, we meticulously extract features. Finally, in the feature circular neighborhood under the log-polar coordinate system, the mapped ADC values are statistically summarized into histograms, resulting in the feature descriptor. Compared with the state-of-the-art descriptors: scale-invariant feature transform (SIFT), a SIFT-like algorithm named OS-SIFT, the frequency-domain descriptor named RIFT and I-KAZE based on anisotropic filtering (AF), the anisotropic diffusion coefficient and orientation histogram (ADCOH) emerges as a superior choice, demonstrating remarkable distinctiveness and robustness.