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GGRNet: Global Graph Reasoning Network for Salient Object Detection in Optical Remote Sensing Images

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13020))

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Abstract

The task of salient object detection (SOD) in optical remote sensing images (RSIs) is more challenging than the SOD in natural sensing images (NSIs) because of the unique characteristics of remote sensing images such as various object scales and background context redundancy. However, the existing methods ignore the global relationship modeling between different salient objects or different parts in one salient object. To this end, we design a Global Graph Reasoning Module (GGRM) in a lightweight and effective form, and propose a novel Global Graph Reasoning Network (GGRNet) for SOD in optical RSIs. During the graph reasoning, the GGRM considers the role of the global information. Specifically, we explore two ways to utilize the global information, including the global features and global nodes, which are ingeniously added to the interaction of graph nodes and fully integrated through iteration. Besides, we stabilize the projection channel between coordinate space and interactive space through an attention mechanism. The GGRNet outperforms the existing state-of-the-art SOD algorithms on two publicly available datasets, and the number of parameters is only 25.01 Mb.

X. Liu and Y. Zhang—Equal contribution.

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Acknowledgement

This work was supported by the Beijing Nova Program under Grant Z2011000068 20016, in part by the National Key Research and Development of China under Grant 2018AAA0102100, in part by the National Natural Science Foundation of China under Grant 62002014, Grant 61532005, Grant 62072026, Grant U1936212, Grant 61971016, Grant U1803264, Grant 61922046, Grant 61772344, Grant 61672443, in part by Elite Scientist Sponsorship Program by the China Association for Science and Technology under Grant 2020QNRC001, in part by Beijing Natural Science Foundation under Grant JQ20022, in part by the Fundamental Research Funds for the Central Universities under Grant 2019RC039, in part by Young Elite Scientist Sponsorship Program by the Beijing Association for Science and Technology, and in part by China Postdoctoral Science Foundation under Grant 2020T130050, Grant 2019M660438.

Author information

Authors and Affiliations

  1. School of Software, Beijing Jiaotong University, Beijing, China

    Xuan Liu

  2. Institute of Information Science, Beijing Jiaotong University, Beijing, China

    Yumo Zhang, Runmin Cong, Chen Zhang, Ning Yang, Chunjie Zhang & Yao Zhao

  3. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China

    Yumo Zhang, Runmin Cong, Chen Zhang, Ning Yang, Chunjie Zhang & Yao Zhao

Authors
  1. Xuan Liu
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  2. Yumo Zhang
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  3. Runmin Cong
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  4. Chen Zhang
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  5. Ning Yang
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  6. Chunjie Zhang
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  7. Yao Zhao
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Corresponding author

Correspondence to Runmin Cong .

Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Liu, X. et al. (2021). GGRNet: Global Graph Reasoning Network for Salient Object Detection in Optical Remote Sensing Images. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_48

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  • DOI: https://doi.org/10.1007/978-3-030-88007-1_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88006-4

  • Online ISBN: 978-3-030-88007-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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