Abstract
Instance segmentation predicts the categories of all instances and locates them using pixel-level masks. Although existing methods have shown exemplary performance, the poor boundaries due to the lack of fine-grained information re-mains a challenge for RSIs instance segmentation. In this paper, to address the problem, we propose a novel instance segmentation branch, namely Mask Decoupled Head, which is mainly composed of a Feature Enhance Module (FEM) and a Feature Decoupled Module (FDM). FEM enhances the rep-resentation of the body features through the low-frequency component of images. FDM decouples the segmentation task by supervising body and edge separately and leverages fine-grained information to complement the boundary details. We performed comprehensive experiments on NWPU VHR -10 and HRSID datasets to evaluate the effectiveness of our pro-posed method and achieved good performance.
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