成像光谱图像安全检索技术的发展与挑战

赵晓蕾; 张菁; 卓力; 陈璐; 耿文浩; 周倩兰; 张洁

doi:10.16383/j.aas.c190319

成像光谱图像安全检索技术的发展与挑战

doi: 10.16383/j.aas.c190319

赵晓蕾^1,,
张菁^{1, 2},
卓力^{1, 2,},
陈璐^1,,
耿文浩^1,,
周倩兰^1,,
张洁^3,

1.
北京工业大学信息学部北京 100124
2.
北京工业大学计算智能与智能系统北京市重点实验室北京 100124
3.
中国地质大学(武汉)资源学院资源信息工程系武汉 430074

基金项目: 国家自然科学基金(61370189), 北京市教育委员会科技计划一般项目 (KM202110005027)资助

详细信息

作者简介:
赵晓蕾：北京工业大学信息学部硕士研究生. 主要研究方向为遥感影像检索. E-mail: zhxl@emails.bjut.edu.cn

张菁：北京工业大学信息学部、计算智能与智能系统北京市重点实验室教授. 美国得州大学圣安东尼奥分校计算机科学系访问学者. 2008年获得北京工业大学电路与系统专业博士学位. 先后发表120多篇期刊和会议论文、出版4部专/译著. 主要研究方向为图像处理, 图像识别, 图像检索. 本文通信作者. E-mail: zhj@bjut.edu.cn

卓力：北京工业大学信息学部、计算智能与智能系统北京市重点实验室教授. 2004年获得北京工业大学模式识别与智能系统专业博士学位. 先后发表180多篇期刊和会议论文、出版6部专著. 主要研究方向为图像/视频编码和传输, 多媒体内容分析, 多媒体信息安全. E-mail: zhuoli@bjut.edu.cn

陈璐：北京工业大学信息学部硕士研究生. 主要研究方向为遥感影像检索. E-mail: chlu@emails.bjut.edu.cn

耿文浩：北京工业大学信息学部硕士研究生.主要研究方向为遥感影像检索. E-mail: gengwh@emails.bjut.edu.cn

周倩兰：北京工业大学信息学部硕士研究生. 主要研究方向为遥感影像检索. E-mail: orchidzql1@emails.bjut.edu.cn

张洁：中国地质大学(武汉)资源学院资源信息工程系讲师. 2005年获得中国地质大学(武汉)地球探测与信息技术专业博士学位. 主要研究方向为遥感图像处理. E-mail: zhangjie0130@126.com

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出版历程
- 收稿日期: 2019-04-23
- 录用日期: 2019-09-09
- 刊出日期: 2021-10-13

Development and Challenge of Secure Spectral Imagery Retrieval Technology

ZHAO Xiao-Lei^1
,,
ZHANG Jing^{1, 2},
ZHUO Li^{1, 2
,},
CHEN Lu^1
,,
GENG Wen-Hao^1
,,
ZHOU Qian-Lan^1
,,
ZHANG Jie^3
,

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124
3.
Department of Resource Information Engineering, School of Earth Resources, China University of Geosciences, Wuhan 430074

Funds: Supported by National Natural Science Foundation of China (61370189) and General Program of Beijing Municipal Education Commission (KM202110005027)

More Information

Author Bio:
ZHAO Xiao-Lei　Master student at the Faculty of Information Technology, Beijing University of Technology. Her main research interest is remote sensing image retrieval

ZHANG Jing　Professor at the Faculty of Information Technology, Beijing University of Technology. She is with the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology. She is also a visiting scholar in the Department of Computer Science, the University of Texas at San Antonio (UTSA). She received her Ph.D. degree in circuit and system from Beijing University of Technology in 2008. She published over 120 refereed journal and conference papers, and has written four book chapters. Her current research interest covers image processing, image recognition, and image retrieval. Corresponding author of this paper

ZHUO Li　Professor at the Faculty of Information Technology, Beijing University of Technology. She is also with the Beijing Key Laboratory of Computational Intelligence and Intelligent System. She received her Ph.D. degree in pattern recognition and intelligent system from Beijing University of Technology in 2004. She published over 180 refereed journal and conference papers, and has written six book chapters. Her research interest covers image/video coding and transmission, multimedia content analysis, and multimedia information security

CHEN Lu　Master student at the Faculty of Information Technology, Beijing University of Technology. Her main research interest is remote sensing image retrieval

GENG Wen-Hao　Master student at the Faculty of Information Technology, Beijing University of Technology. His main research interest is remote sensing image retrieval

ZHOU Qian-Lan　Master student at the Faculty of Information Technology, Beijing University of Technology. Her main research interest is remote sensing image retrieval

ZHANG Jie　Lecturer in the Department of Resource Information Engineering, School of Earth Resources, China University of Geosciences. She received her Ph.D. degree in geodetection and information technology from China University of Geosciences in 2005. Her main research interest is remote sensing image processing

摘要

摘要: 随着遥感对地观测技术的飞速发展, 成像光谱图像呈现指数增长, 特别是人工智能技术和高性能计算的加速崛起, 进一步推动了成像光谱大数据时代的到来. 因此, 如何高效地组织和管理海量的成像光谱图像数据成为一个亟待解决的实际应用问题. 然而, 网络时代的开放性与共享性, 使得网络信息安全问题日益突出, 特别是含有重要信息的成像光谱图像应具有严格的保密性, 确保检索过程中不发生失泄密事件. 本文总结了近年来成像光谱图像安全检索的主要技术, 包括特征提取与表示、特征降维、加密域安全检索技术和性能评价准则, 最后对成像光谱图像安全检索技术进行了总结与展望.
- 成像光谱图像 /
- 安全检索 /
- 特征提取与表示 /
- 特征降维 /
- 加密域
Abstract: 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.
- Spectral imagery /
- secure retrieval /
- feature extraction and representation /
- feature dimensionality reduction /
- encrypted domain

HTML全文

图 1 成像光谱图像端元提取流程

Fig. 1 The flowchart of end member extraction ofspectral imagery

下载: 全尺寸图片幻灯片

图 2 光谱单词特征构建流程

Fig. 2 The flowchart of spectral words creation

下载: 全尺寸图片幻灯片

图 3 成像光谱图像与其光谱单词直方图

Fig. 3 The spectral imagery and the histogram of the spectral words

下载: 全尺寸图片幻灯片

图 4 用于提取深度光谱−空间特征的DCGAN网络模型

Fig. 4 The DCGAN model for extracting deep spectral-spatial features

下载: 全尺寸图片幻灯片

图 5 五种方法的查准查全率曲线

Fig. 5 The precision-recall curves of fivedifferent methods

下载: 全尺寸图片幻灯片

图 6 t-SNE的降维流程图

Fig. 6 The dimensionality reduction method of t-SNE based nonlinear Hashing

下载: 全尺寸图片幻灯片

图 7 4种哈希降维方法的mAP分数

Fig. 7 The mAP of the four Hashing methods

下载: 全尺寸图片幻灯片

图 8 加密域图像安全检索基本框架

Fig. 8 The framework of secure image retrieval based on feature encryption

下载: 全尺寸图片幻灯片

图 9 两种不同加密方法的查全率−查准率曲线

Fig. 9 The precision-recall curves of two differentfeature encryption methods

下载: 全尺寸图片幻灯片

表 1 两种不同特征加密方法的加密时间和检索时间比较 (s)

Table 1 The time cost of feature encryption andretrieval between two different methods (s)

方法特征加密时间加密后检索时间

特征随机化加密 5.0×10⁻³ 1.0

保序加密 1.10 3.0

下载: 导出CSV

参考文献(82)

[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, 2017, 14(10): 1775-1779 doi: 10.1109/LGRS.2017.2734960
[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, 2014, 11(1): 249-253 doi: 10.1109/LGRS.2013.2255097
[3]	Zhou W X, Li C M. Deep feature representations for high-resolution remote-sensing imagery retrieval. Remote Sensing, 2016, 9(5): 489-493
[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, 2015, 35(3): 274-281 doi: 10.1108/SR-10-2014-0716
[5]	Omruuzun F, Demir B, Bruzzone L, Cetin Y Y. Content based hyperspectral image retrieval using bag of endmembers image descriptors. In: Proceedings of the 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Los Angeles, CA, USA: IEEE, 2017. 1−4
[6]	Li C, Ma Y, Mei X G. Hyperspectral image classification with robust sparse representation. IEEE Geoscience and Remote Sensing Letters, 2017, 13(5): 641-645
[7]	Ye G D, Huang X L. An image encryption algorithm based on autoblocking and electrocardiography. IEEE MultiMedia, 2016, 23(2): 64-71 doi: 10.1109/MMUL.2015.72
[8]	Kaufman J, Weinheimer J. J, Celenk M. Spatial-spectral feature extraction on hyperspectral imagery. In: Proceedings of the 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Lausanne, Switzerland: IEEE, 2014. 1−4
[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, 2017, 56(4): 1909-1921
[10]	罗小波. 遥感图像智能分类及其应用. 北京: 电子工业出版社, 2011. Luo Xiao-Bo. Intelligent Classification and Application of Remote Sensing Image. Beijing: Electronic Industry Press, 2011.
[11]	Jiang H Z, Yoon S C, Zhuang H, Wang W, Lawrence K C. Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging.Meat Science, 2018, 139: 82-90 doi: 10.1016/j.meatsci.2018.01.013
[12]	Cross G R, Jain A K. Markov random field texture models. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1983, 5(1): 25-39
[13]	Tan Y M, Xia W, Xu B, Bai L J. Multi-feature classification approach for high spatial resolution hyperspectral images. Journal of the Indian Society of Remote Sensing, 2017, 46(3): 1-9
[14]	Benediktsson J A, Palmason J A, Sveinsson J R. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 480-491 doi: 10.1109/TGRS.2004.842478
[15]	Segl K, Roessner S, Heiden U, Kaufmann H. Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data. Journal of Photogrammetry & Remote Sensing, 2005, 58(1): 99-112
[16]	Plaza A J, Bruzzone L. Content-based hyperspectral image retrieval using spectral unmixing. In: Proceedings of the 2011 Conference on Image and Signal Processing for Remote Sensing XVⅡ. Prague, Czechrepublic: SPIE, 2011. 81800O
[17]	王强. Hyperion高光谱数据进行混合像元分解研究 [硕士学位论文]. 东北林业大学, 中国, 2006. Wang Qiang. The Study on Unmixing of Mixed Pixels Based on Hyperion Data [Master thesis], Northeast Forestry University, China, 2006.
[18]	Wang W Y, Cai G Y. Endmember extraction by pure pixel index algorithm from hyperspectral image. In: Proceedings of the 2008 International Conference on Optical Instruments and Technology. Beijing, China: SPIE, 2008. 71570E
[19]	Zhou Q L, Zhang J, Tian Q, Zhuo L, Geng W H. Automatic endmember extraction using pixel purity index for hyperspectral imagery. In: Proceedings of the 22nd International Conference on Multimedia Modeling. Miami, USA: Springer, 2016. 207−217
[20]	Zhang J, Zhou Q L, Zhuo L, Geng W H, Wang S Y. A CBIR system for hyperspectral remote sensing images using endmember extraction. International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31(4): 1-15
[21]	周倩兰, 基于多特征的成像光谱图像检索系统的设计与实现 [硕士学位论文]. 北京工业大学, 中国, 2016. Zhou Qian-Lan. A Spectral Imagery Retrieval System Based on Multi-feature [Master thesis], Beijing University of Technology, China, 2016.
[22]	Zhang J, Geng W H, Liang X, Li J F, Zhuo L, Zhou Q L. Hyperspectral remote sensing images retrieval system using spectral and texture feature. Applied Optics,2017, 56(16): 4785-4796 doi: 10.1364/AO.56.004785
[23]	Geng W H, Zhang J, Zhuo L, Liu J H, Chen L. Creating spectral words for large-scale hyperspectral remote sensing image retrieval. In: Proceedings of the 2016 Pacific-Rim Conference on Multimedia (PCM). Xi＇ an, China: Springer, 2016. 116−125
[24]	耿文浩, 面向安全检索的成像光谱图像加密技术研究 [硕士学位论文]. 北京工业大学, 中国, 2017. Geng Wen-Hao. Research on Encryption Technologies of Spectral imagery for Secure Retrieval [Master thesis], Beijing University of Technology, China, 2017.
[25]	Zhang J, Geng W H, Liang X, Zhuo L, Chen L. Secure retrieval method of hyperspectral image in encrypted domain. Journal of Applied Remote Sensing, 2017, 11(3): 035021
[26]	Gemmeke J F, Virtanen T, Hurmalainen A. Exemplar-based sparse representations for noise robust automatic speech recognition. IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(7): 2067-2080 doi: 10.1109/TASL.2011.2112350
[27]	Felzenszwalb P F, Girshick R B, McAllester D, Ramanan D. Object detection with discriminatively trained part-based models.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645 doi: 10.1109/TPAMI.2009.167
[28]	Le C Y, Bengio Y, Hinton G. Deep learning.Nature, 2015, 521(7553): 436-444 doi: 10.1038/nature14539
[29]	Dong L, Wang R, Chen X Y, He L, Zhang Q, Cao X, Izquierdo E. ILPN: An independent losses pose net for globally location body joints. In: Proceedings of the 5th International Conference on Computational Visual Media. Tianjin, China, 2017.
[30]	Dong L, Wang R, Chen X Y, He L, Zhang Q, Cao X, Izquierdo E. ADORE: An adaptive holons representation framework for human pose estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 28(10): 2803-2813
[31]	Iqbal U, Milan A, Gall J. PoseTrack: Joint multi-person pose estimation and tracking. In: Proceedings of the 2017 IEEE Conferemce on Computer Vision and Pattern Recognition, Honolulu, HI, USA: IEEE, 2017.4654−4663
[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, 2016, 51: 163-170 doi: 10.1016/j.engappai.2016.01.022
[33]	张洪群, 刘雪莹, 杨森, 李宇. 深度学习的半监督遥感图像检索. 遥感学报, 2017, 21(3): 406-414 Zhang Hong-Qun, Liu Xue-Ying, Yang Sen, Li Yu. Semi-supervised remote sensing image retrieval for deep learning. Remote Sensing, 2017, 21(3): 406-414
[34]	Liang X D, Gong K, Shen X H, Lin L. Look into Person: Joint body parsing & pose estimation network and a new benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(4): 871-885
[35]	Rumelhart D. E, Hinton G. E, Williams R. J. Learning representations by back-propagating errors. Nature, 1986, 323: 533-536 doi: 10.1038/323533a0
[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, 2017, 14(11): 1928-1932 doi: 10.1109/LGRS.2017.2737823
[37]	Zhong P, Gong Z Q, Li S T, Schönlieb C. Learning to diversify deep belief networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3516−3530
[38]	Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2013, 18(7): 1527-1537
[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, 2017, 14(3): 404-408 doi: 10.1109/LGRS.2016.2645708
[40]	Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolution generative adversarial networks. In: Proceedings of the 2016 International Conference on Learning Representations, San Juan, Puerto Rico: ADM, 2016. 1−16
[41]	Chen L, Zhang J, Liang X, Li J F, Zhuo L. Deep spectral-spatial feature extraction based on DCGAN for hyperspectral image retrieval. In: Proceedings of the 15th IEEE International Conference on Pervasive Intelligence and Computing (PICom), Orlando, USA: IEEE, 2017. 752−759
[42]	陈璐, 基于深度特征的成像光谱图像安全检索系统设计与实现 [硕士学位论文]. 北京工业大学, 中国, 2018. Chen Lu. A Secure Spectral Imagery Retrieval System Based on Deep Features [Master thesis], Beijing University of Technology, China, 2018.
[43]	Zhang J, Chen L, Zhuo L, Liang X, Li J F. An efficient hyperspectral image retrieval method: Deep spectral-spatial feature extraction with DCGAN and dimensionality reduction using t-SNE based NM hashing. Remote Sensing, 2018, 10(2): 271−278
[44]	Veganzones M A, Grana M. A spectral/spatial CBIR system for hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2012, 5(2): 488-500
[45]	Grana M, Veganzones M A. An endmember-based distance for content based hyperspectral image retrieval. Pattern Recognition, 2012, 45(9): 3472-3489 doi: 10.1016/j.patcog.2012.03.015
[46]	Chen Y S, Zhao X, Jia X P. Spectral-spatial classification of hyperspectral data based on deep belief network. Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2381-2392
[47]	Maaten L V D, Postma E, Herik J V D. Dimensionality reduction: A comparative review. Review Literature and Arts of the Americas, 2009, 10(1): 1-23
[48]	Plaza A, Plaza J, Martin G. Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data. In: Proceedings of the 2009 IEEE International Workshop on Machine Learning for Signal Processing. Grenoble, France: IEEE, 2009. 1−6
[49]	Emre D O, Begum D, Bulent S, Lorenzo B. A novel system for content-based retrieval of single and multi-label high-dimensional remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(7): 2473-2490 doi: 10.1109/JSTARS.2018.2832985
[50]	曹嫣, 基于光谱显著性的成像光谱图像目标检测技术的研究 [硕士学位论文]. 北京工业大学, 中国, 2015. Cao Yan. Research on Target Detection for Spectral Imagery Based on Spectral Saliency [Master thesis], Beijing University of Technology, China, 2015.
[51]	Nielsen A A. Kernel maximum autocorrelation factor and minimum noise fraction transformations. IEEE Transactions on Image Processing, 2011, 20(3): 612-624 doi: 10.1109/TIP.2010.2076296
[52]	Law M H C, Jain A K. Incremental nonlinear dimensionality reduction by manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(3): 377−391
[53]	Nezhad M M, Gironacci E, Rezania M. Stochastic modelling of crack propagation in materials with random properties using isometric mapping for dimensionality reduction of nonlinear data sets. International Journal for Numerical Methods in Engineering, 2017, 113(7): 656-680
[54]	Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326 doi: 10.1126/science.290.5500.2323
[55]	Hinton G, Roweis S. Stochastic neighbor embedding. Advances in Neural Information Processing Systems, 2002, 41(4): 833-840
[56]	Van Der Maatem L,Hinton G . Visualizing data using t-SNE. Journal of Machine Learning Research, 2017, 9(2605): 2579-2605
[57]	Du P, Wang X, Tan K, Xia J. Dimensionality reduction and feature extraction from hyperspectral remote sensing imagery based on manifold learning. Geomatics and Information Science of Wuhan University, 2011, 36(2): 148-152
[58]	胡英杰. 基于局部线性嵌入的高光谱端元提取算法研究 [硕士学位论文]. 华中科技大学, 中国, 2015. Hu Ying-Jie. The Research of Endmembers Extraction from Hyperspectral Remote Sensing Image Based on Locally Linear Embedding [Master dissertation], Huazhong University of Science and Technology, China, 2015.
[59]	Yang M S, Nataliani Y. Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recognition, 2017, 71: 45-59
[60]	Weiss Y, Torralba A, Fergus R. Spectral Hashing. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems.Vancouver, British, Columbia, Canada: CAI, 2008. 1753−1760
[61]	Zhang D, Wang J, Cai D, Lu J S. Self-taught Hashing for fast similarity search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Geneva, Switzerland: ACM, 2010. 18−25
[62]	Liu W, Wang J, Kumar S, Chang S F. Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning. Washington, USA: ACM, 2011. 1−8
[63]	Geng W H, Zhang J, Chen L, Li J F, Zhuo L. Hybrid domain encryption method of hyperspectral remote sensing image. In: Proceedings of the 2017 Pacific-Rim Conference on Multimedia. Harbin, China: Springer, 2017. 890−899
[64]	Li C Q, Lin D D, Lv J H, Hao F. Cryptanalyzing an image encryption algorithm based on autoblocking and electrocardiography. IEEE Multimedia, 2018, 25(4): 46−56
[65]	Rivest R L, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 1978, 21(2): 120-126 doi: 10.1145/359340.359342
[66]	Gentry C. Fully homomorphic encryption using ideal lattices. In: Proceedings of the 41st ACM Symposium on Theory of Computing. Bethesda, USA: ACM, 2009. 169−178
[67]	Vercauteren F. Fully homomorphic encryption with relatively small key and ciphertext sizes. Lecture Notes in Computer Science, 2010, 6056: 420-443
[68]	张燕, 基于同态加密的图像安全检索技术研究 [硕士学位论文]. 北京工业大学, 中国, 2014. Zhang Yan. Research on Technologies of Secure Image Retrieval Based on Homomorphic Encryption [Master thesis], Beijing University of Technology, China, 2014.
[69]	Zhang Y, Zhuo L, Peng Y F, Zhang J. A secure image retrieval method based on homomorphic encryption for cloud computing. In: Proceedings of the 19th International Conference on Digital Signal Processing. Hong Kong, China: IEEE, 2014. 269−274
[70]	Peng Y, Hui L I, Cui J, Zhang J W, Ma J F, Peng C G. hOPE: improved order preserving encryption with the power to homomorphic operations of ciphertexts. Information Sciences, 2017, 60(6): 116-132
[71]	Zhou Y, Feng D, Hua Y, Xia W, Fu M. A similarity-aware encrypted deduplication scheme with flexible access control in the cloud. Future Generation Computer Systems, 2017, 84: 177-189
[72]	Lu W J, Varna A L, Swaminathan A, Wu M. Secure image retrieval through feature protection. In: Proceedings of the 2009 International Conference on Acoustics, Speech, and Signal Processing. Taipei, China: IEEE, 2009. 1533−1536
[73]	Zhuo L, Mao N S, Zhang J. Bit-sensitivity based video encryption scheme in compressed domain. International Journal of Advancements in Computing Technology, 2012, 4(8): 155-164 doi: 10.4156/ijact.vol4.issue8.19
[74]	Tang Z, Wang F, Zhang X. Image encryption based on random projection partition and chaotic system. Multimedia Tools and Applications, 2016, 76(6): 1-27
[75]	Xia Z, Wang X, Zhang L. A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Transactions on Information Forensics and Security, 2016, 11(11): 2594-2608 doi: 10.1109/TIFS.2016.2590944
[76]	Lu W J, Varna A L, Min W. Confidentiality-preserving image search: Acomparative study between homomorphic encryption and distance-preserving randomization. IEEE Access, 2014, 2: 125-141 doi: 10.1109/ACCESS.2014.2307057
[77]	Zhang J, Chen L, Liang X, Zhuo L, Tian T. Hyperspectral image secure retrieval based on encrypted deep spectral-spatial features. Journal of Applied Remote Sensing, 2019, 13(1): 185-189
[78]	Pareek N K, Patidar V, Sud K K. Image encryption using chaotic logistic map. Image and Vision Computing, 2006, 24(9): 926-934 doi: 10.1016/j.imavis.2006.02.021
[79]	Agrawal R, Kiernan J, Srikant R, Xu Y R. Order preserving encryption for numeric data. In: Proceedings of the 2004 SIGMOD International Conference on Management of Data. Paris, France: ACM, 2004. 563−574
[80]	杨国铮, 禹晶, 肖创柏, 孙卫东. 基于形态字典学习的复杂背景SAR图像舰船尾迹检测. 自动化学报, 2017, 43(10): 1713-1725 Yang Guo-Zheng, Yu Jing, Xiao Chuang-Bai, Sun Wei-Dong. Ship wake detection in SAR images with complex background using morphological dictionary learning. Acta Automatica Sinica, 2017, 43(10): 1713-1725
[81]	安文, 刘昆, 王杰. 基于自适应采样的多假设预测残差重构算法研究. 自动化学报, 2017, 43(12): 2190-2201 An Wen, Liu Kun, Wang Jie. Research on multi-hypothesis residual reconstruction algorithm based on adaptive sampling. Acta Automatica Sinica, 2017, Acta Automatica, 2017, 43(12): 2190-2201
[82]	张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望. 自动化学报, 2018, 44(6): 961-977 Zhang Hao-Kui, Li Ying, Jiang Ye-Nan. Deep learning for hyperspectral imagery classification: the state of the art and prospects. Acta Automatica Sinica, 2018, 44(6): 961-977