Generation of Attributes for Highly Imbalanced Land Cover Data

Through the rise of new remote sensing datasets with sufficient size for current deep learning models, land cover classification results have improved in recent years. Unfortunately, earth exhibits a natural imbalance in different land cover classes, also visible in these datasets. In the domain of zero-shot learning, image attributes have enabled better results in transfer learning by creation of a mid level representation between different classes. This representation can also be constructed for land cover data and used to detect minority classes through shared features. We propose a way for generation of attributes by text mining for one of the biggest land cover datasets. With these attributes we achieve state-of-the-art performance in land cover classification and improve results especially for minority classes. Further, we show that these attributes have great potential in weakly supervised land cover segmentation.