Automated Indunation Mapping: Comparison of Methods
Asmamaw Gebrehiwot, Leila Hashemi-Beni
Abstract
High-resolution imagery is increasingly used to detect flooded areas during a crisis situation. The article presents a comparison of four image classification methods for flood extent mapping. The methods include Random Forest (RF), support vector machine (SVM), fully convolutional network (FCN), and normalized difference water index (NDWI). High-resolution UAV imagery collected during Hurricane Matthew (2016) flood events were used to evaluate the classification methods for generating an accurate flood extent map. In this study, a fully convolutional network fine-tuned to segment the inundation areas. RF, SVM, and NDWI are implemented using the same dataset used for mapping flood extents. The results show that the FCN achieved an overall accuracy of 97.72%, followed by NDWI with 96.0%, SVM with 88.9%, and 87.8 % of RF. The results imply that FCN is more efficient than RF, SVM, and NDWI on generating real-time flood extent maps.