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Machine Learning techniques are popular and effective means for Land Use Land Cover (LULC) classification using remotely sensed data. These techniques are capable to deal with high-dimensional data, which is a major concern in the domain of remote sensing. Literature indicated the effectiveness of Machine Learning (ML) for complex classification of land cover classes. However, it is a challenging issue to select the appropriate classifier. The main objective of this paper is to implement and evaluate the most popular machine-learning techniques for LULC classification using remotely sensed data. The selected ML techniques are Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Random Forest. The chosen study area is Nanital district situated in Uttarakhand, India. In this research work, Sentinel-2 satellite data (10m spatial resolution) has been utilized. Results revealed that Random Forest achieved the highest accuracy (OA) of 91.56% (kappa value of 0.899). It is found that the ANN classifier produced the lowest classification accuracy of 88.16 and a kappa value of 0.859. Other classifiers like SVM and kNN obtained an OA of 90.72% and 89.64

Author

Rashmi Saini; Shivam Rawat

Publisher

IEEE

Published

2023

Language

EN