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
  • [1] Liu Z, Tang B, He X C, Qiu Q C, Wang H J, Sparse tensor-based dimensionality reduction for hyperspectral spectral-spatial discriminant feature extraction, IEEE Geoscience and Remote Sensing Letters, 14, 10, pp. 1775-1779, (2017)
  • [2] Zhang E L, Zhang X R, Yang S Y, Wang S, Improving hyperspectral image classification using spectral information divergence, IEEE Geoscience and Remote Sensing Letters, 11, 1, pp. 249-253, (2014)
  • [3] Zhou W X, Li C M, Deep feature representations for high-resolution remote-sensing imagery retrieval, Remote Sensing, 9, 5, pp. 489-493, (2016)
  • [4] Shao Z F, Zhou W X, Cheng Q W, Diao C Y, An effective hyperspectral image retrieval method using integrated spectral and textural features, Sensor Review, 35, 3, pp. 274-281, (2015)
  • [5] Omruuzun F, Demir B, Bruzzone L, Cetin Y Y., Content based hyperspectral image retrieval using bag of endmembers image descriptors, Proceedings of the 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp. 1-4, (2017)
  • [6] Li C, Ma Y, Mei X G, Hyperspectral image classification with robust sparse representation, IEEE Geoscience and Remote Sensing Letters, 13, 5, pp. 641-645, (2017)
  • [7] Ye G D, Huang X L, An image encryption algorithm based on autoblocking and electrocardiography, IEEE MultiMedia, 23, 2, pp. 64-71, (2016)
  • [8] Kaufman J, Weinheimer J. J, Celenk M., Spatial-spectral feature extraction on hyperspectral imagery, Proceedings of the 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-4, (2014)
  • [9] Liu B, Yu X C, Zhang P Q, Yu A Z, Fu Q Y, Wei X P, Supervised deep feature extraction for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 56, 4, pp. 1909-1921, (2017)
  • [10] Luo Xiao-Bo, Intelligent Classification and Application of Remote Sensing Image, (2011)