OBBInst: Remote sensing instance segmentation with oriented bounding box supervision

Remote sensing (RS) instance segmentation is an important but challenging task due to multi-oriented, densely arranged objects and lack of mask annotation. Compared with redundant horizontal bounding-box (HBB) and expensive pixel-level annotation, oriented bounding box (OBB) annotations can provide compact object depicts with lower annotation costs. Therefore, we propose the first weakly supervised remote sensing instance segmentation method with OBB supervision (namely OBBInst) to reduce the annotation burden and make full use of existing abundant OBB annotations. Based on BoxInst (a high-performance instance segmentation method with box annotations), OBBInst has customized a framework for OBB annotation to unify the incompatibility between existing HBB-based and OBB-based methods. In addition, we propose an oriented projection method with a corresponding loss function to achieve more precise target depicts of OBB annotation. Moreover, we propose an edge similarity loss to incorporate Canny edge prior into deep learning framework for more precise edge identification of densely arranged objects. We have conducted extensive experiments on iSAID and HRSC datasets, and the experimental results demonstrate that OBBInst can achieve the state-of-the-art performance as compared to existing box-supervised methods. In addition, OBBInst dramatically narrows the performance gap between weakly and fully supervised instance segmentation (23.9% vs. 35.6% in iSAID dataset and 79.5% vs. 84.9% in HRSC dataset).