Abstract
ABSTRACT Waterbody extraction from satellite imagery plays a crucial role in various environmental monitoring and management applications. Accurate identification and delineation of water bodies are essential for assessing water resources, monitoring changes in aquatic ecosystems, and supporting decision-making processes. This review presents a comprehensive analysis of different methods used for waterbody extraction from satellite images, highlighting their strengths, limitations, and recent advancements. This review begins by discussing traditional methods, such as thresholding-based methods, machine learning methods, and object-based image analysis, which have been widely employed in the past. Consequently, the focus shifts towards, how deep learning models, such as convolutional neural networks (CNNs) have been applied to improve waterbody extraction accuracy and address challenges posed by spectral variations, cloud cover, and sensor limitations. Overall, this review serves as a valuable resource for researchers, practitioners, and decision-makers involved in water resource management and environmental monitoring.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
