Sea surface Fresnel reflections difference driven de-glint algorithm for airborne optical images.

This paper presents a glint correction algorithm for high spatial resolution optical remote sensing imagery captured by the ER-2 Airborne Visual Infrared Imaging Spectrometer (AVIRIS). The algorithm employs linear and differential techniques to mitigate sun glint and sky glint effects, encompassing statistical glint reflections resulting from variations in imaging angles within strips and inter-strip variations due to Fresnel reflectance disparities. It aims to diminish Fresnel reflectance diversity on water surfaces and mitigate the distortions induced by glint reflectance during spectral and ocean color inversion. A comparative analysis of spectral and ocean color information in AVIRIS images before and after correction reveals enhanced accuracy following the glint correction. By systematically addressing multiple glint reflections and their ramifications, this method offers a valuable framework for correcting water surface glint in diverse high spatial resolution optical imagery.

Extraction of eutrophic and green ponds from segmentation of high-resolution imagery based on the EAF-Unet algorithm.

Inland ponds exhibit remarkable ubiquity across the globe, playing a vital role in the sustainability of global continental freshwater resources and contributing significantly to their biodiversity. Numerous ponds are eutrophic and experience recurrent seasonal or year-round algal blooms or persistent duckweed cover, conferring a characteristic green hue. Here, we denote these eutrophic and green ponds as EGPs. The excessive proliferation of algal blooms and duckweed within these EGPs poses a significant threat to the ecological functioning of these aquatic systems, which can lead to hypoxia or the release of microcystins. To identify these EGPs automatically, we constructed an Efficient Attention Fusion Unet (EAF-Unet) algorithm using Gaofen-2 (GF2) panchromatic and multispectral imagery. The attention mechanism was incorporated in Unet to help better detect EGPs. Using the first EGP labeled dataset, we determined the best input feature combination (RGB, NIR, NDVI, and Bright) and the most effective encoding (Rasnet50) for EAF-Unet for distinguishing EGPs from other ground cover types. The evaluation indices - Precision (0.81), Recall (0.79), F1-Score (0.80), and Intersection over Union (IoU, 0.67) - indicate that EAF-Unet can accurately and robustly extract EGPs from GF2 images without relying on pond water masks. Remote-sensing EGP products can assist in identifying ponds with severe eutrophication. Moreover, these products can serve as references for identifying high-risk areas prone to improper sewage discharge or inadequate sewer construction.