UNCERTAINTY-GUIDED REPRESENTATION LEARNING IN LOCAL CLIMATE ZONE CLASSIFICATION

A significant leap forward in the performance of remote sensing models can be attributed to recent advances in machine and deep learning. Large data sets particularly benefit from deep learning models, which often comprise millions of parameters. On which part of the data a machine learner focuses on during learning, however, remains an open research question. With the aid of a notion of label uncertainty, we try to address this question in local climate zone (LCZ) classification. Using a deep network as a feature extractor, we identify data samples that are seemingly easy or hard to classify for the model and base our experiments on the relatively more uncertain samples. For training of the network, we make use of distributional (probabilistic) labels to incorporate the voter confusion directly into the training process. The effectiveness of the proposed uncertainty-guided representation learning is shown in context of active learning framework where we show that adding more certain data to the training pool increases model performance even with the limited data.