Advancing sun glint correction in high-resolution marine UAV RGB imagery for coral reef monitoring

Abstract

Sun glint presents a significant challenge in marine ecological remote sensing by obscuring valuable features of benthic communities, thus hindering accurate monitoring of these communities. More specifically, sun glint leads to inaccurate coral reef identification when it comes to utilize the high-resolution marine Unmanned Aerial Vehicle (UAV) imagery. Despite the availability of many physical models that address this problem using ocean remote sensing satellite images, there is still a lack of effective sun glint correction solutions for high-resolution RGB imagery obtained by consumer-grade UAVs in marine ecological monitoring, particularly at the centimeter level. In this study, an innovative sun glint correction pipeline combining a Foreground Attention-based Semantic Segmentation Network (FANet) with optical flow-based pixel propagation has been proposed, aiming to achieve accurate sun glint detection and restoration of real texture of coral reefs. To further understand the inner mode of sun glint segmentation FANet model, we conduct explainable analysis to ensure that it learns the informative sun glint features and makes reliable decisions. According to the comprehensive experimental evaluation using three datasets, our FANet model exhibits accurate and effective sun glint detection, achieving an IoU range of 70.86%–83.96%. Moreover, the sun-glint free images processed by our sun glint correction method have been employed for large-scale image stitching and shallow-water coral reef habitat semantic mapping. The results demonstrate that our sun glint correction approach successfully restores the texture of coral reefs and prepares a reliable foundation for subsequent downstream applications. We believe that our method as an open-source sun glint correction solution in high-resolution marine UAV RGB images is able to enhance the accuracy and efficiency of various marine ecological remote sensing tasks at a low-cost. The relevant code can be found in https://github.com/jyqinnn/Sun-glint-correction.