Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery

Abstract

Water body extraction plays a key role in water resource management and development which is one of the key issues in research topics related to remote sensing in recent decades. Pixel-based; supervised and unsupervised classification methods and water indices have been developed for extracting water body from satellite imagery while for some cases they are unable to differentiate water body from low albedo features with different spectral characteristics. The object of this paper is to evaluate the efficiency of object-based, pixel-based and water indices for water body extraction. Landsat-8 imagery used for this study which has 30m resolution in Visible, NIR (Near-Infrared), SWIR (Shortwave Infrared) spectrum and panchromatic band with 15m resolution. The Edge-Based Segmentation (EBS) algorithm and the Support Vector Machine (SVM) classification method has been used for object-based approach, on the other hand, Maximum Likelihood and K-means methods has been selected for pixel-based classification. As a water index, Normalized Difference Water Index (NDWI) has been selected to extract water body from satellite imagery. The study area selected in both urban and mountainous regions, with different characteristics, which located in Turkey. Results show that object-based water body extraction is more accurate than the other methods tested.