Enhanced Remote Sensing Instance Segmentation with Feature Fusion

Zhiwei Tao, Yangyang Li, Xuanting Hao, Bin Wu, Ronghua Shang, Licheng Jiao

Abstract: Instance segmentation in the field of remote sensing imagery is recognized as a complex and difficult task. Previous approaches suffer from inadequate feature fusion, insufficient learning of shape information, and lack of segmentation of object edges. To address these challenges, we introduce FEA-Net(Fusion Edge-Aware Instance Segmentation Network), a multiple information fusion model for remote sensing image instance segmentation. Our model makes the predicted instance masks more accurate and can effectively improve the instance segmentation performance of high-resolution remote sensing images. We have evaluated our method on two datasets, NWPU VHR-10, and the iSAID. The experimental results demonstrate the effectiveness of our method, showing strong performance.