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An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration
J. Y. Lu; H. G. Jia; T. Li; Z. Q. Li; J. Y. Ma and R. F. Zhu
2021
发表期刊Remote Sensing
卷号13期号:9页码:21
摘要Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features for local area matching. Then, a feature-based method combined with the instance segmentation results is adopted to acquire more accurate local feature matching. Finally, multi-constraints based on the instance segmentation results are introduced to work on the outlier removal. In the experiments of high-resolution remote sensing image registration, the proposal effectively copes with the circumstance of the sensed image with poor positioning accuracy. In addition, the method achieves superior accuracy and competitive robustness compared with state-of-the-art feature-based methods, while being rather efficient.
DOI10.3390/rs13091657
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收录类别SCI ; EI
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/65349
专题中国科学院长春光学精密机械与物理研究所
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J. Y. Lu,H. G. Jia,T. Li,et al. An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration[J]. Remote Sensing,2021,13(9):21.
APA J. Y. Lu,H. G. Jia,T. Li,Z. Q. Li,&J. Y. Ma and R. F. Zhu.(2021).An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration.Remote Sensing,13(9),21.
MLA J. Y. Lu,et al."An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration".Remote Sensing 13.9(2021):21.
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