SAR-optical deep UNet matching with Gabor jet model

With the drastic increase of Remote Sensing payloads both in terms of satellite count and advancements of sensing technologies and resolution, automatic matching and registration of Synthetic Aperture Radar (SAR)-Optical images for mission-critical applications with time efficiency and high accuracy is a must. Optical and SAR sensors utilise distinct imaging techniques. As a result, the process of matching images from these two modalities is not only less precise but also requires a significant amount of time. In this study, we introduce an innovative algorithm that combines deep learning methods with the traditional Gabor Jet Model. This is achieved by employing cross entropy loss function to calculate the anticipated shift. The encoder-decoder structure is used to map non-linear dependencies and maintain both local and global data. Current state-of-the-art (SoTA) methods focus on either the spatial or temporal domain. However, our approach integrates both the spatial and temporal domains, preserving both global and local characteristics for comparison. Experimental results show that our proposed algorithm attains pixel-level precision and surpasses the current SoTA methods.