Towards Tackling Multi-Label Imbalances in Remote Sensing Imagery

Recent advances in automated image analysis have lead to an increased number of proposed datasets in remote sensing applications. This permits the successful employment of data hungry state-of-the-art deep neural networks. However, the Earth is not covered equally by semantically meaningful classes. Thus, many land cover datasets suffer from a severe class imbalance. We show that by taking appropriate measures, the performance in the minority classes can be improved by up to 20 percent without affecting the performance in the majority classes strongly. Additionally, we investigate the use of an attribute encoding scheme to represent the inherent class hierarchies commonly observed in land cover analysis.