Performance of different machine learning algorithms on satellite image classification in rural and urban setup

In many countries, the increasing population drives the spatiotemporal land use and land cover change (LULCC) at a higher rate, causing heterogeneous landscape. Hence, the LULCC is a dynamic and frequent process causing fragmented land cover, and therefore, extensive research on LULCC pattern is necessary at different spatial and temporal scales. Moreover, it is essential to identify appropriate algorithms to detect LULCC in such fragmented areas. Furthermore, the rate of change is different in rural and urban areas. Hence, the main goal of the study was to describe the performance of different machine learning algorithms on three different spatial and multispectral satellite image classification in rural and urban extents. We carried out atmospheric and geometric correction. To achieve this, we…