Supervised Classification Accuracy Assessment Using Remote Sensing and Geographic Information System

Abstract

Assessing the accuracy of classification algorithms is paramount as it provides insights into reliability and effectiveness in solving real-world problems. Accuracy examination is essential in any remote sensing-based classification practice, given that classification maps consistently include misclassified pixels and classification misconceptions. In this study, two imaginary satellites for Duhok province, Iraq, were captured at regular intervals, and the photos were analyzed using spatial analysis tools to provide supervised classifications. Some processes were conducted to enhance the categorization, like smoothing. The classification results indicate that Duhok province is divided into four classes: vegetation cover, buildings, water bodies, and bare lands. During 2013-2022, vegetation cover increased from 63% in 2013 to 66% in 2022; buildings roughly increased by 1% to 3% yearly; water bodies showed a decrease of 2% to 1%; the amount of unoccupied land showed a decrease from 34% to 30%. Therefore, the classification accuracy was assessed using the approach of comparison with field data; the classification accuracy was about 85%.