Classification of Land Cover in Sentinel-2 Imagery Using Machine Learning Models

Remote sensing data of medium resolution are commonly used to classify land cover, and machine learning (ML) models have taken on a central aspect in the necessary data analysis. Ordinarily, land cover is coded on a pixel basis on the basis of Digital Number (DN) values, which in turn are computed across several spectral bands. This paper is concerned with land cover mapping in Mosul, Iraq, based on satellite images captured by Sentinel-2. Two platforms featuring unsupervised classification algorithms were used, Google Earth Engine and ArcMap, making it possible to use K-means and X-means in Google Earth Engine and ISO Cluster in ArcMap. The comparative analysis of these algorithms allowed development of four significant land cover classes: built-up areas, vegetation, water bodies, and bare land. The results of the classification for the Mosul study area also indicated that using X-means gave an accuracy of 76.34, while using ISO Cluster gave 73.61, and K-means gave 80.14. The highest classification accuracy was achieved by the use of K-means, suggesting that it is the best algorithm to use in this region