Open AccessA Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan
Author(s) -
Fatima Mushtaq,
Khalid Mahmood,
Mohammad Chaudhry Hamid,
Rahat Tufail
Publication year - 2021
Publication title -
pakistan journal of scientific and industrial research. series a: physical sciences
Language(s) - English
Resource type - Journals
eISSN - 2223-2559
pISSN - 2221-6413
DOI - 10.52763/pjsir.phys.sci.64.3.2021.265.274
Subject(s) - support vector machine , land cover , cohen's kappa , artificial intelligence , pattern recognition (psychology) , classifier (uml) , kappa , computer science , mathematics , machine learning , statistics , data mining , land use , engineering , civil engineering , geometry
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.