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AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images

文献类型：期刊论文

作者Ding, Kaimeng1,2; Chen, Shiping3; Wang, Yu4; Liu, Yueming2; Zeng, Yue1; Tian, Jin1
刊名REMOTE SENSING
出版日期2021-12-01
卷号13期号:24页码:26
关键词security of remote sensing images deep learning subject-sensitive hashing integrity authentication perceptual hash U-Net
DOI10.3390/rs13245109
通讯作者Zeng, Yue(zengy@jit.edu.cn)
英文摘要The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between "subject-related tampering" and "subject-unrelated tampering" of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net.
WOS关键词NETWORK ; SEGMENTATION ; FUSION ; IMPACT ; RIVER
资助项目National Natural Science Foundation of China[41801303] ; National Natural Science Foundation of China[42101428] ; Scientific Research Fund of Jinling Institute of Technology[jit-fhxm-201604] ; Scientific Research Fund of Jinling Institute of Technology[jit-b-201520] ; Qing Lan Project
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000742132400001
出版者MDPI
资助机构National Natural Science Foundation of China ; Scientific Research Fund of Jinling Institute of Technology ; Qing Lan Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/169672]  
专题中国科学院地理科学与资源研究所
通讯作者Zeng, Yue
作者单位1.Jinling Inst Technol, Nanjing 211169, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resource & Environm Informat Syst, Beijing 100101, Peoples R China
3.CSIRO Data61, Sydney, NSW 1710, Australia
4.Changjiang Nanjing Waterway Bur, Nanjing 210011, Peoples R China
推荐引用方式
GB/T 7714		 Ding, Kaimeng,Chen, Shiping,Wang, Yu,et al. AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images[J]. REMOTE SENSING,2021,13(24):26.
APA Ding, Kaimeng,Chen, Shiping,Wang, Yu,Liu, Yueming,Zeng, Yue,&Tian, Jin.(2021).AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images.REMOTE SENSING,13(24),26.
MLA Ding, Kaimeng,et al."AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images".REMOTE SENSING 13.24(2021):26.

入库方式： OAI收割

来源：地理科学与资源研究所

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