Selection of Algorithm for Land Use Land Cover Classification and Change Detection
Arnab Ghosh Chowdhury, Prof. Gowdagede Siddaramaiah Dwarakish
Abstract
Land use land cover (LULC) classification is an important research criterion for urban growth modelling. In this study, five different parametric classification algorithms (Maximum Likelihood Classifier, Mahalanobis Distance, Minimum Distance, Spectral Angle Mapper, Spectral Correlation Mapper) are used for the LULC classification of the Barrackpore subdivision of West Bengal state in India. Two different Landsat datasets (Landsat5 and Landsat8) are used for 2005 and 2020. Various algorithms have shown good accuracy for different LULC feature classes. Minimum Distance and Maximum Likelihood Classifier (MLC) has given the highest overall accuracy in 2005 and 2020, respectively. The best classification results are used for the change detection in the LULC classes over fifteen years (2005-2020). The classification result will help to choose a suitable algorithm for a regional level LULC study in further research. The change detection result indicates the need for a good growth pattern in the region.