Abstract:
Accurate and precise identification of water bodies from remote sensing imagery plays
an important role in various fields like environmental monitoring, resource management,
and disaster assessment. This study presents a comprehensive exploration of two distinct
yet interconnected approaches: semantic segmentation and fractional mapping, aimed at
enhancing the precision and applicability of water body analysis. The primary purpose
of this research is to use RGB image datasets instead of multi-spectral water bodies
datasets. The research focuses on using robust and computationally efficient U-Net
architecture. This choice is rooted in its ability to achieve efficient semantic segmentation
results while demanding significantly fewer training resources.
In addition to semantic segmentation, this study extends its focus to fractional mapping,
an approach aimed at quantifying the distribution and extent of water bodies through
continuous values. However, the unavailability of suitable datasets for fractional map ping poses a challenge. To solve this, a custom dataset of 100 lakes is created using
satellite imagery from planet.com. The research uses various preprocessing techniques
and loss functions, ensuring optimal results.
For fractional mapping of water bodies, we used the same U-Net model which was
adopted for semantic segmentation of water bodies. The research used different stan dard loss functions and introduced novel hybrid loss functions, tailored to segmentation.
The trained model’s performance is evaluated on unseen datasets, with semantic seg mentation inference conducted on test data, and fractional mapping inference carried
out on the Namal Lake dataset. For semantic segmentation, the U-Net model shows
the best results across IoU-Focal-LCD loss in terms of validation IoU coefficient which
is 79% and Dice-Focal loss shows the best value for validation Dice coefficient which is
83%. For fractional mapping, the U-Net model with Huber loss shows 85% validation
and 95% training accuracy.
