Enhanced Remote Sensing Instance Segmentation with Feature Fusion

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-N...

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Bibliographic Details
Published inIEEE International Geoscience and Remote Sensing Symposium proceedings pp. 7107 - 7110
Main Authors Tao, Zhiwei, Li, Yangyang, Hao, Xuanting, Wu, Bin, Shang, Ronghua, Jiao, Licheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2024
Subjects
Accuracy
Feature fusion
Image edge detection
Instance segmentation
Location awareness
Prediction algorithms
Predictive models
Remote sensing images
Shape
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ISSN2153-7003
DOI10.1109/IGARSS53475.2024.10640731

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Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10640731