Classification of Imbalanced Land-Use/Land-Cover Data Using Variational Semi-Supervised Learning
Tjeng Wawan Cenggoro, Sani M. Isa, Gede Putra Kusuma, Bens Pardamean
International Conference on Innovative and Creative Information Technology 2017
Abstract: Classification of Land Use/Land Cover (LULC) data is a typical task in remote-sensing domain. However, because the classes distribution in LULC data is naturally imbalance, it is difficult to do the classification. In this paper, we employ Variational Semi-Supervised Learning (VSSL) to solve imbalance problem in LULC of Jakarta City. This VSSL exploits the use of semi-supervised learning on deep learning model. Therefore, it is suitable for classifying data with abundant unlabeled like LULC. The result shows that VSSL achieves 80.17% of overall accuracy, outperforming other algorithms in comparison.
