Comparative Analysis of Classification Algorithms for Landuse / Landcover Change Over A Part of The East Coast Region of Tamil Nadu And Its Environs

The Landuse/Landcover (LULC) changes become more intense in this era due to rapid urbanization, industrialization and over utilization of agricultural land for human wellbeing. This study is an attempt to find an effective approach among various classifiers for the evaluation of spatio-temporal variations in LULC over a part of the East coastal region of Tamil Nadu for the period of 30 years. High and low resolution remote sensing data are used to perform five different LULC classification algorithms: K-means, IsoData, Maximum Likelihood (ML), Parallelepiped (PP) and Support Vector Machine (SVM). The experimental outcomes conclude that the Support vector machine classifier comparatively shows high accuracy and classification performance than others.