Weakly Supervised Segmentation of Small Buildings With Point Labels

Jae-Hun Lee, ChanYoung Kim, Sanghoon Sull; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7406-7415

Most supervised image segmentation methods require delicate and time-consuming pixel-level labeling of building or objects, especially for small objects. In this paper, we present a weakly supervised segmentation network for aerial/satellite images, separately considering small and large objects. First, we propose a simple point labeling method for small objects, while large objects are fully labeled. Then, we present a segmentation network trained with a small object mask to separate small and large objects in the loss function. During training, we employ a memory bank to cope with the limited number of point labels. Experiments results with three public datasets demonstrate the feasibility of our approach.