Assessment of Sun Glint Correction Methods in Unmanned Aerial Vehicle-Based Ocean Optical Remote Sensing

The sun glint poses a significant challenge in optical remote sensing of the ocean using unmanned aerial vehicles (UAVs). It contaminates the oceanographic information within the images, not only obscuring underwater seafloor features but also increasing the radiance reflectance within the images, which is detrimental for the study and monitoring of the marine environment. Currently, there are two main approaches for sun glint correction in optical imagers (in this context, referring to visible light images captured in RGB channels): methods based on sea surface statistical model and methods based on deep learning. To assess the potential application of sun glint correction methods on UAV RGB imagery, this paper reviews and summarizes both types of methods. Through experimental evaluation, our examines the usability of these methods in UAV-based ocean remote sensing, providing a scientific foundation for obtaining high-quality marine monitoring images. The experimental results indicate that traditional sea surface statistical model-based methods developed for satellite imagery face challenges when transitioning to high-resolution UAV imagery. Conversely, deep learning-based methods show promise for sun glint correction in UAV RGB imagery. Although these methods are still in the early exploration stage, they hold the potential to offer innovative and efficient solutions for mitigating sun glint effects in high-resolution UAV RGB imagery.