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

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