  Deep learning and remote sensing techniques have significantly advanced water
monitoring abilities; however, the need for annotated data remains a challenge.
This is particularly problematic in wetland detection, where water extent
varies over time and space, demanding multiple annotations for the same area.
In this paper, we present DeepAqua, a self-supervised deep learning model that
leverages knowledge distillation (a.k.a. teacher-student model) to eliminate
the need for manual annotations during the training phase. We utilize the
Normalized Difference Water Index (NDWI) as a teacher model to train a
Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture
Radar (SAR) images, and to train the student model, we exploit cases where
optical- and radar-based water masks coincide, enabling the detection of both
open and vegetated water surfaces. DeepAqua represents a significant
advancement in computer vision techniques by effectively training semantic
segmentation models without any manually annotated data. Experimental results
show that DeepAqua outperforms other unsupervised methods by improving accuracy
by 7%, Intersection Over Union by 27%, and F1 score by 14%. This approach
offers a practical solution for monitoring wetland water extent changes without
needing ground truth data, making it highly adaptable and scalable for wetland
conservation efforts.
