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

Jannath Firthouse Mohammed Yashin, Aarthi Deivanayagam, Abdul Rahaman Sheik Mohideen, Jegankumar Rajagopal
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引用次数: 2

Abstract

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.
泰米尔纳德邦东海岸及周边部分地区土地利用/覆被变化分类算法的比较分析
在这个时代,由于快速的城市化、工业化和对农业用地的过度利用,土地利用/土地覆盖(LULC)的变化变得更加激烈。本研究试图在各种分类器中寻找一种有效的方法来评估泰米尔纳德邦东部沿海地区部分地区30年的LULC时空变化。使用高分辨率和低分辨率遥感数据执行五种不同的LULC分类算法:K-means, IsoData,最大似然(ML),平行六面体(PP)和支持向量机(SVM)。实验结果表明,支持向量机分类器具有较高的准确率和分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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