Accuracy Assessment of Unsupervised Land Use and Land Cover Classification Using Remote Sensing and Geographical Information Systems

Abstract

A significant tool for determining how accurate a categorization product is the accuracy assessment. Remote Sensing is one of the essential tools for compiling land use and land cover maps through image classification. The availability of high-quality Landsat imagery and secondary data, an accurate classification technique, and the user's knowledge and competence with the procedures essential to the image classification process. Assess the satellite image classification suitability for further mapping and analysis through accuracy assessments. This paper examined land use and land cover classification using unsupervised classification and extracted NDBI and NDVI further to support main land use and land cover types in the area. The accuracy of classifications was assessed using an error matrix and Kappa statistics. Land use and land cover, NDBI, and NDVI classification accuracies are almost perfect, further verified by the Kappa statistics tool. An excellent unsupervised classification of land use and land cover classes was generated. Accuracy assessment evaluation is one of the most significant tools for determining a classification product's accuracy. The confusion error matrix and Kappa coefficient were particularly useful in calculating accuracy assessment. Typically, accuracy measures of the unsupervised classification show a moderate accuracy level. This study observed almost perfect agreement in all types of accuracy measures. This study is an important source of information that planners and decision-makers may utilize to plan the environment sustainably.