Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study

Abstract

This study used three different classification models, namely Support Vector Machine (SVM), Random Forest Machine (RFM), and Maximum Likelihood (ML) for classification of Landsat (7 & 8), and Sentinel-2A data sets. Each case’s area of interest (AOI) and number of training sets (within fixed AOI of Chennai district boundary) were considered equal. Land use class change was observed because of rapid urbanization and developmental activities under urbanization, and the LULC was monitored using the ArcGIS Pro platform for 2005, 2010, 2015 and 2020. The overall accuracy (OA) of the first, second, and third was 89%, 88%, 82%, 80% under RF, and 87%, 85%, 79%, 80% under SVM. However, the ML classifier provided the OA as 82%, 77%, 76%, 66% for 2005, 2010, 2015 and 2020, respectively. The Kappa coefficient (K) was calculated under the first, second, and third, as 84%, 79%, 75%, 72%, under RF, and 80%, 78%, 71%, 67% under SVM. However, the ML provided a K value of 77%, 67%, 67%, 57% for 2005, 2010, 2015 and 2020. Based on the quantitative assessments, the RF classifier showed good accuracy, then SVM and ML in classifications of fixed AOI with fixed training sets.