Ensemble Classification Technique for Water Detection in Satellite Images

Abstract

Satellite images are capable of providing valuable, synoptic coverage of the environment and so have been used for natural disaster assessment such as flooding. There are plenty of machine learning classifiers that can detect water in satellite images and although none are perfect they often produce acceptable results. Ensemble classifiers combine multiple classifiers and are often able to outperform their constitute classifiers. Ensemble classifiers are known to be effective for image classification in different applications but are unexplored for water detection in satellite images. This research employs an ensemble classifier to detect water in satellite images for flood assessment. Classification was performed both using individual bands and Normalized Difference Water Index (NDWI). The results show that to improve the classification accuracy with ensemble classifiers it is important to choose appropriate classifiers to ensemble. It also shows that this approach is capable of producing good classification accuracy for a seen location when bands are used and an unseen location when NDWI is used.