Development and Challenge of Secure Spectral Imagery Retrieval Technology

被引：0
|
作者：
Zhao X.-L. [1 ]
Zhang J. [1 ,2 ]
Zhuo L. [1 ,2 ]
Chen L. [1 ]
Geng W.-H. [1 ]
Zhou Q.-L. [1 ]
Zhang J. [1 ,2 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing
[3] Department of Resource Information Engineering, School of Earth Resources, China University of Geosciences, Wuhan
来源：
Zhang, Jing (zhj@bjut.edu.cn); Zhang, Jing (zhj@bjut.edu.cn) | 1600年 / Science Press卷 / 47期
基金：
中国国家自然科学基金;
关键词：
Encrypted domain; Feature dimensionality reduction; Feature extraction and representation; Secure retrieval; Spectral imagery;
D O I：
10.16383/j.aas.c190319
中图分类号：
学科分类号：
摘要：
With the rapid development of remote-sensing technology for earth observation, spectral imagery data presents exponential growth. The accelerated rise of artificial intelligence technology and high-performance computing has further promoted the arrival of the big data era of spectral imagery. Therefore, how to organize and manage the massive spectral imagery data efficiently has become an urgent practical application problem. However, because the openness and sharing of the network era makes the security of network information increasingly prominent, especially for the spectral imagery containing important information, it should have strict confidentiality to ensure that no leakage of information in the retrieval process. This paper summarizes the main techniques of spectral imagery secure retrieval in recent years, including feature extraction and representation, feature dimensionality reduction, secure retrieval in encryption domain and performance evaluation criteria. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2090 / 2102
页数：12
相关论文
共 82 条
  • [31] Iqbal U, Milan A, Gall J., PoseTrack: Joint multi-person pose estimation and tracking, Proceedings of the 2017 IEEE Conferemce on Computer Vision and Pattern Recognition, pp. 4654-4663, (2017)
  • [32] Dong L, Feng N, Quan P J, Kong G P, Chen X Y, Zhang Q N, Optimal kernel choice for domain adaption learning, Engineering Applications of Artificial Intelligence, 51, pp. 163-170, (2016)
  • [33] Zhang Hong-Qun, Liu Xue-Ying, Yang Sen, Li Yu, Semi-supervised remote sensing image retrieval for deep learning, Remote Sensing, 21, 3, pp. 406-414, (2017)
  • [34] Liang X D, Gong K, Shen X H, Lin L, Look into person: Joint body parsing and pose estimation network and a new benchmark, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 4, pp. 871-885, (2018)
  • [35] Rumelhart D. E, Hinton G. E, Williams R. J., Learning representations by back-propagating errors, Nature, 323, pp. 533-536, (1986)
  • [36] Zhang X R, Liang Y J, Li C, Ning H Y, Jiao L C, Zhou H Y., Recursive auto encoders-based unsupervised feature learning for hyperspectral image classification, IEEE Geoscience and Remote Sensing Letters, 14, 11, pp. 1928-1932, (2017)
  • [37] Zhong P, Gong Z Q, Li S T, Schonlieb C, Learning to diversify deep belief networks for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 55, 6, pp. 3516-3560, (2017)
  • [38] Hinton G E, Osindero S, Teh Y W, A fast learning algorithm for deep belief nets, Neural Computation, 18, 7, pp. 1527-1537, (2013)
  • [39] Jiang J J, Chen C, Yu Y, Jiang X W, Ma J Y, Spatial-aware collaborative representation for hyperspectral remote sensing image classification, IEEE Geoscience and Remote Sensing Letters, 14, 3, pp. 404-408, (2017)
  • [40] Radford A, Metz L, Chintala S., Unsupervised representation learning with deep convolution generative adversarial networks, Proceedings of the 2016 International Conference on Learning Representations, pp. 1-16, (2016)
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