Integrating various satellite images for identification of the water bodies through using machine learning: A case study of Salah Adin, Iraq

This research aims to extract water bodies from several types of satellite images by using machine learning. There are several methods to apply the extraction of information about water bodies such as unsupervised classification, supervised classification. This project applied the supervised classification method to extract water bodies and building geodatabase for the water bodies in Salah Adin, Iraq by applying remote sensing and GIS technique. The satellite images which have been used in this research include satellite images from RapideEye satellite with spatial resolution (5×5) m at 2011, where these images used to extract canals of water, also used images from Sentinel-2 satellite with spatial resolution (10 × 10) m at 2017 to extract lakes, and broad rivers. In addition, DEM raster (90) m at 2011 from ASTER satellite was used to extract the water streams, which are expected to be channels of water discharge in flood situations. During implementation of the methodology of the research the most important issue which appear is using multi data to extract all types of water bodies in study area also to avoid the pixel mixed problem, this case was evident when using RapidEye satellite images in the confluence of the river in the surrounding wetlands, which led to inaccurate results in the geometric dimensions of the river, although the high spatial resolution but the influenced element on the accuracy of results is radiometric resolution. The result shows the random forest of machine learning algorithm is overcome on the other algorithms such as decision tree machine learning, maximum likelihood and support vector machine. The high accuracy of image classification to extract the water bodies depend on integrating the three satellite images.