Abstract:
Most of the previous research was related to non-parametric classification methods, states that the
Maximum Likelihood (ML) and Support Vector Machine (SVM) methods are the leading
classification methods in producing high accuracy. However, only a small proportion of studies
have compared the performance of these two methods using multitemporal remote sensing imagery,
particularly on Sentinel-2 and Landsat data. This study tries to evaluate and compare the
performance of ML and SVM classifiers to mapping land use/cover using Sentinel-2 and Landsat
multitemporal imagery data. The Tabunio watershed with an area of 62.586 ha has been mapped
with ten types of land use/cover are water body, forest, bare land, residential, plantations,
agriculture, swamps, shrubs, pond, and mining. The confusion matrix and the Kappa coefficient
was used to assess classification accuracy. All classification results show a high overall accuracy
(OA) ranging from 86% to 95%. Among the two classifiers, four data series with different images
and sample sizes, SVM produced the highest OA than ML.
