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Instance Segmentation with Oriented Proposals for Aerial Images

Instance segmentation is a challenging issue in remote sensing. The existing state-of-the-art methods use horizontal bounding box (HBB) to infer the instance mask of object. However, the objects in the aerial images have the characteristics of being distributed in arbitrary orientation, densely packed, and so on. In this case, the HBB usually contains a lot of background information and several neighboring objects, leading to coarse and inaccurate mask prediction. To solve the aforementioned problems, we propose a new instance segmentation method, ISOP, by inferring the mask on oriented bounding box (OBB) instead of HBB. We show that the proposed method leading to more accurate mask predictions, especially for densely packed objects. We evaluate our method in the iSAID dataset, and compared to the baseline, the ISOP has achieved around 17% improvement in terms of mAP and 11% for densely packed objects.