Critical Review Analysis on Deep Learning-Based Segmentation Techniques for Water-Body Extraction

Abstract

The rapid advancement in the applications of remote sensing imagery had attracted considerable attention from researchers for digital image analysis. Researchers had performed the surveying and delineation of water bodies with excellent efforts and algorithms in the past, but they faced many challenges due to the varying characteristics of water such as its shape, size, and flow. Traditional methods employed for water body segmentation posed certain limitations in terms of accuracy, reliability, and robustness. Rapid growth in the automation category allowed researchers to incorporate deep learning models into the segmentation analysis. Deep learning segmentation models for water body feature extraction have shown promising results based on accuracy and precision. This chapter presents a brief review on the deep learning models used for water-body extraction with their merits over the traditional approaches. It also discusses existing results with challenges faced and future scope.