Automatic delineation of water bodies using Corine data from remotely sensed images

Water resources is crucial for the continuity of life. Therefore, mapping water resources is required. Successful analysis of remotely sensed images can provide reliable information for water researches. However, it is very complex process to ensure that the maps created are not affected by shadows, cloud or other noise. In addition, it is necessary to successfully map all water types in various geographies. It is important that the method used is practical so that scientists who are not image analysts can use the large data pool provided by satellite images. In this paper, a novel algorithm for water body extraction from Landsat imagery is proposed. In this method, Corine data are used as auxiliary data to automatically generate training data. Four study areas with different characteristics, from different parts of the world, are used to test the proposed method. The results obtained are compared with other automatic classification methods.