Context Union Edge Network for Semantic Segmentation of Small-Scale Objects in Very High Resolution Remote Sensing Images

Semantic segmentation of small-scale objects in very high resolution (VHR) remote sensing images plays an important role in some special tasks, such as change detection and mapping of land cover. However, due to small size, small-scale objects are more likely to be completely obscured by shadows than large-scale objects, which make it difficult for the traditional convolutional neural network (CNN) to distinguish small-scale objects from shadows. Furthermore, even if small-scale objects are distinguished, their boundaries are still difficult to refine. To solve the above problems, a novel context union edge network (CEN) for small-scale objects semantic segmentation is proposed by comprehensively considering both the contextual and edge information. In CEN, a plug-and-play context-based feature enhancement module (CFEM) is designed to enhance the ability of CNNs to distinguish small-scale objects. Then, an information exchange mechanism (IEM) is proposed based on the dual-stream (semantic and edge stream) network to refine the boundaries of small-scale objects. Finally, some experiments based on the ISPRS Vaihingen data set are conducted in terms of both overall accuracy (OA) and F1-score. The proposed CEN achieves 89.9% of F1-score for small-scale objects (cars) and 90.9% of OA, harvesting new state-of-the-art results.