A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan | [Pakistan Journal of Scientific and Industrial Research (Series A: Physical Sciences) • 2021]

Author(s):

1. Fatima Mushtaq: Center for Geographical Information System, University of the Punjab,Lahore,Pakistan

2. Khalid Mahmood: Department of Space Science, University of the Punjab, Lahore, Pakistan; Remote Sensing, GIS and Climatic Research Laboratory, University of the Punjab, Lahore, Pakistan

3. Mohammad Hamid Chaudhry: Center for Geographical Information System, University of the Punjab,Lahore,Pakistan

4. Rahat Tufail: College of Earth and Environmental Science, University of the Punjab,Lahore,Pakistan

Abstract:

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.

Page(s): 265-274

DOI: DOI not available

Published: Journal: Pakistan Journal of Scientific and Industrial Research (Series A: Physical Sciences), Volume: 64, Issue: 3, Year: 2021

Keywords:

land cover , Accuracy assessment , Support Vector Machine , maximum likelihood classification , kappa statistics

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