Unsupervised Semantic Segmentation of Aerial Images With Application to UAV Localization

Abstract

Recent advances and applications of aerial image semantic segmentation have yielded an increase in their use in day-to-day tasks. However, state-of-art algorithms, composed mostly of deep semantic segmentation networks (DSSNs), may not be suitable for application domains in which labels (targeted output masks) are scarce. This work proposes a fully unsupervised semantic segmentation method able to find the appropriate number of semantics labels (yielded from clustering) without the need of a previously annotated dataset. Once the semantic labels are identified, a classifier trained from these labels can be used to assign semantics to new input images. In contrast to other clustering-based segmentation methods, our method does not require any input parameter to find the clustering partition that best accommodates data. Empirical results on two real datasets showed that the use of semantics in a template-matching-based unmanned aerial vehicle (UAV) localization pipeline reduced the overall position estimation error.