A light-weight model to generate NDWI from Sentinel-1
Journal:
arXiv
Published Date:
Jan 23, 2025
Abstract
The use of Sentinel-2 images to compute Normalized Difference Water Index
(NDWI) has many applications, including water body area detection. However,
cloud cover poses significant challenges in this regard, which hampers the
effectiveness of Sentinel-2 images in this context. In this paper, we present a
deep learning model that can generate NDWI given Sentinel-1 images, thereby
overcoming this cloud barrier. We show the effectiveness of our model, where it
demonstrates a high accuracy of 0.9134 and an AUC of 0.8656 to predict the
NDWI. Additionally, we observe promising results with an R2 score of 0.4984
(for regressing the NDWI values) and a Mean IoU of 0.4139 (for the underlying
segmentation task). In conclusion, our model offers a first and robust solution
for generating NDWI images directly from Sentinel-1 images and subsequent use
for various applications even under challenging conditions such as cloud cover
and nighttime.
