Classification of Rare Building Change Using CNN with Multi-Class Focal Loss

In the remote sensing, supervised deep learning has recently achieved great success of information extraction. However, it requires a large training data in order to effectively learn. In building change classifications, collecting such training data is an extremely expensive and time-consuming process, because of the rarity of positive classes. Learning of a data set including rare classes has two major problems, (1) class imbalance and (2) overfitting. In this study, we verify the effectiveness of focal loss in the building change classification. From our experimental results, not only the class imbalance but also the overfitting is affected the down-weighting effect of the focal loss. The focal loss automatically adjusts learning speed for each class.