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 multi-source 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 an 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, within the Harris scale space, rooted in the ASS, we meticulously extract features. Finally, within the feature circular neighborhood under the log-polar coordinate system, the mapped ADC values are statistically summarized into histograms, resulting in the feature descriptor. Comparing 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, the ADCOH emerges as a superior choice, demonstrating remarkable distinctiveness and robustness.