Deep learning techniques became crucial in analyzing satellite images for various remote sensing applications such as water body detection. Water body segmentation helps identify and analyze the statistics of various water bodies such as rivers, lakes, and reservoirs. Remote sensing-based real-time water body detection aids in providing a proper response during crises such as floods and course changes in rivers. However, the need for high-resolution multichannel satellite images is the main challenge in achieving a highly accurate water body segmentation. Most water body extraction methods described in the literature use multi-band satellite data that gather extra information from additional bands. However, the lack of such a dataset poses a significant challenge to the analysis. As a result, the research in this field is considerably weaker compared with the other related disciplines. The current study focuses on a research problem for segmenting water body regions from relatively low