SUn Glint Aware Restoration (SUGAR)

A sweet, and simple algorithm for sun glint correction of high resolution UAV imagery (applicable for both multispectral (MSI) and hyperspectral images (HSI) with sub-meter spatial resolution)

Source: Pak, H.Y., Law, A.WK., Lin, W. et al. Sun Glint-Aware Restoration (SUGAR): a robust sun glint correction algorithm for UAV imagery to enhance monitoring of turbid coastal environments. Environ Monit Assess 197, 254 (2025). https://doi.org/10.1007/s10661-025-13702-6

Tutorial

• A "How-to-use" guide provided by the sugar notebook - SUGAR.ipynb

Comments

• The only required input that SUGAR requires is a numpy array of $(m,n,c)$, where $m, n$ are the spatial dimensions of the image, and $c$ is the number of channels of the image (applicable for multispectral/hyperspectral images)
• The nature of the input type is a reflectance image (ranges from 0 - 1), with dtype==float

Set up

MicaSense usage

• Requirements follow that of MicaSense image processing library
• Install ExifTool
• See MicaSense setup
• In git bash, cd to your selected directory
• git clone https://github.com/pakhuiying/SUGAR.git
• cd SUGAR
• Be sure to check the requirements required if you are using MicaSense MSI imageries
• Create a virtual environment with: conda env create -f sgc.yml
• conda activate SUGAR

Enquiries

Feel free to email [email protected] for any issues/bugs encountered, or just file an issue on github directly