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.

1. INTRODUCTION

Marine ecosystems cover approximately 71% of the Earth's surface, playing an irreplaceable role in maintaining global ecological balance, regulating climate, and providing essential resources crucial for human survival. In the process of human development and the protection of oceans, marine environmental monitoring proves essential for early warning and evaluation of ocean hydro-meteorological conditions, climate change, and ecosystem disasters [1]. While satellite remote sensing technology remains a primary method for achieving large-scale coverage marine environmental monitoring, its effectiveness is constrained by challenges such as low spatial resolution, restricted temporal coverage, and susceptibility to cloud cover and adverse weather conditions. Nowadays, the advancement of Unmanned Aerial Vehicle (UAV) remote sensing technology has introduced new tools for marine environmental monitoring. The characteristics of easy deployment, strong maneuverability, low data acquisition costs and high signal-to-noise ratio make UAVs equipped with optical cameras widely applicable in various domains, including marine environmental monitoring, marine mapping, and marine disaster monitoring [2].