Optimum supervised classification algorithm identification by investigating PlanetScope and Skysat multispectral satellite data of Covid lockdown

This research identifies the optimum supervised classification algorithm based on modeling Covid 19 lockdown situations all around the World. The deadly Covid 19 viruses suddenly stopped the fast-moving world and all the commercial and noncommercial activities were stalled for an uncertain period during 2020-2021. In this work, object-based image classification approaches have been used to compare pre-Covid and post-Covid (at the time lockdown) images of the study area. These study areas are Washington DC, USA, Sao Paulo, Brazil, Cairo, Egypt, Afghanistan/Iran border, and Beijing, China. All the study areas possess different geographical conditions but have a similar situation of Covid 19 lockdowns. Six supervised image classification techniques are known as Parallelepiped classification ([Formula: see text]), Minimum distance classification ([Formula: see text]), Mahalanobis distance classification ([Formula: see text]), Maximum likelihood classification ([Formula: see text]), Spectral angle mapper classification ([Formula: see text]) and Spectral information divergence classification ([Formula: see text]) are used to classify the satellite data of the study area. Thus based on classification results and statistical features, it has been observed that [Formula: see text] has obtained the least significant results. In contrast, the most reliable results and highest classification accuracies are obtained through [Formula: see text] , [Formula: see text] , and [Formula: see text] classification algorithms.