supervised classifier

Identification and monitoring of surface water using remote sensing have become very important in recent decades due to its importance in human needs and political decisions. Therefore, surface water has been studied using remote sensing systems and Sentinel-1 and Sentinel-2 sensors in this study. In this paper, two data fusion approaches and decision fusion improve the accuracy of surface water identification for the two study areas in Mazandaran province, Iran, due to dense forests, turbid rivers, and land Agricultural products have been proposed. Data fusion approach by increasing the spatial resolution of Sentinel-2 optical sensor from 20 meters to 10 meters and using radar dataset and water indices in one layer, monitoring surface water with much higher accuracy by classification using supervised classifiers such as Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) were performed. Then, by combining the results using the Maximum Voting (MV) method, the accuracy of the results compared to the data fusion approach increased by 4 percent to 5 percent. Since narrow and confused rivers are not easily identifiable and extractable, the proposed approach was able to extract an accurate map of surface water.