Water Bodies Identification in Landsat 8 OLI Image Using Machine Learning

Water is one of the important natural resources on the earth which is used for various purposes. But human activities and climate change cause reduction of surface water. So water bodies monitoring and management are essential. In recent years, machine learning methods have given better performance than other methods to classify an image. In this study, two machine learning methods - Classification And Regression Trees (CART) and Support Vector Machine (SVM) are used to identify water bodies in Landsat 8 OLI images. The performances of the models are compared with water index methods and evaluated based on overall accuracy, F1 score and kappa coefficient. For machine learning models two test sets are used. One is the samples of own dataset and another is completely new unseen samples to check how the models perform when there are unknown samples. The evaluated result shows that, though CART can achieve higher accuracy among all the methods when samples of own dataset are used, its performance decreased when unseen samples are used. But SVM has given similar performance for both test sets and its accuracy is more than 98%.