EN
柳思聪
副教授
航天遥感智能信息处理理论方法与应用研究
姓名: 柳思聪
性别:

职称:

副教授

籍贯:

浙江海宁

邮箱:

sicong.liu@tongji.edu.cn

简介:

主要从事航天遥感智能信息处理理论方法与应用研究,研究方向包括遥感光谱探测、多模态遥感信息融合、时序遥感变化监测等。主持国家自然科学基金和国家重点研发计划项目子课题等7个项目。发表SCI论文80余篇。担任第十届国际多时相遥感影像分析会议(MultiTemp)技术主席,是《IEEE GRSL》等多个国际SCI期刊编委和专刊特邀主编。

学习和工作经历

  • 2020.12 - 至今      bat365官网登录 副教授

  • 2017.04 - 2020.12 bat365官网登录 助理教授

  • 2015.10 - 2017.03 bat365官网登录 助理研究员

  • 2011.11 - 2015.02 意大利特伦托大学(University of Trento)博士,信息与通讯技术

  • 2009.09 - 2011.06 中国矿业大学,硕士,摄影测量与遥感

  • 2005.09 - 2009.06 中国矿业大学,学士,地理信息系统

获奖与荣誉

  • 第九届高校GIS论坛“高校GIS新锐”;

  • 入选2021年度上海市青年科技启明星计划;

  • 2019年测绘科技进步奖一等奖(排名第1),“地表要素遥感精细识别与动态监测方法研究与应用”;

  • 2019年湖南省科技进步二等奖(排名第8),“空天地一体化地理矿情可靠性监测关键技术及重大工程应用”;

  • 2018年江苏省科技进步三等奖(排名第5),“城市环境遥感关键技术与应用”;

  • 2014-2016年度国际SCI学术期刊《Information Fusion》高被引论文认证;

  • 2014IEEE GRSS Data Fusion ContestIEEE国际数据融合大赛)论文组竞赛季军。

研究方向

  • 月球与行星遥感光谱探测;

  • 多模态遥感信息融合与应用;

  • 遥感场景-要素-物质智能识别与解译;

  • 多时相遥感动态监测技术及应用;

  • 图像智能理解与深度模式识别。

主要学术兼职

  • 委员,中国测绘学会深空探测遥感测绘工作委员会;

  • 委员,中国图象图形学学会遥感图像专业委员会;

  • 委员,中国遥感应用协会高光谱遥感技术与应用专业委员会;

  • 第十届国际多时相遥感影像分析会议(10th International Workshop on the Analysis of Multitemporal Remote Sensing Images - MultiTemp 2019)技术主席;

  • 2020年至今,国际光学工程学会“遥感图像和信号处理”遥感国际会议(SPIE Remote Sensing Digital Forum:Image and Signal Processing for Remote Sensing)程序委员会委员;

  • 会员,电气电子工程师学会(IEEE);

  • 会员,IEEE地球科学与遥感协会;

  • 会员,IEEE GRSS图像分析与数据融合技术委员会(IADF-TC);

  • 会员,国际光学工程学会(SPIE);

  • 担任10多个国际、国内学术会议分会场主席;

  • 担任50多个国际、国内学术期刊和国内外会议审稿人。

主要项目

  • 国家自然科学基金面上项目,融合多时相深浅特征的高光谱卫星遥感影像自动变化检测方法研究(42071324),2021.1~2024.12,主持;

  • 地表精细遥感变化检测的多视角层次分析方法研究,国家自然科学基金青年基金(41601354, 2017.01-2019.12, 主持;

  • 卫星颤振影响修正,国家重点研发计划项目子课题(2017YFB05029033),2017.06-2020.12,主持;

  • 多时相遥感影像变化特征集构建及优化,bat365官网登录青年优秀人才培养行动计划项目(22120180005),2018.01-2019.12,主持。

主要论文

  • H. Zhao, S. Liu, Q. Du, L. Bruzzone, Y. Zheng, K. Du, X. Tong, H. Xie, X. Ma, "GCFnet: Global Collaborative Fusion Network for Multispectral and Panchromatic Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 5632814.

  • S. Liu, Y. Zheng, Q. Du, L. Bruzzne, A. Samat, X. Tong, Y. Jin, C. Wang, “A Shallow-to-Deep Feature Fusion Network for VHR Remote Sensing Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 5410213.

  • S. Liu, F. Bovolo, L. Bruzzone, X. Tong, Q. Du, “Foreword to the Special Issue on Recent Advances in Multitemporal Remote Sensing Data Processing,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.15, pp: 776-778, 2022.

  • S. Liu, H. Zhao, Q. Du, L. Bruzzone, A. Samat and X. Tong, "Novel Cross-Resolution Feature-Level Fusion for Joint Classification of Multispectral and Panchromatic Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 5619314.

  • Y. Zheng, S. Liu, Q. Du, H. Zhao, X. Tong and M. Dalponte, "A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 10691-10704, 2021.

  • S. Liu, Y. Zheng, Q. Du, A. Samat, X. Tong, M. Dalponte, “A Novel Feature Fusion Approach for VHR Remote Sensing Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.14, pp:464-473, 2021.

  • S. Liu, Y. Zheng, M. Dalponte, X. Tong, “A Novel Fire Index based Burned Area Change Detection Approach Using Landsat-8 OLI Data,” European Journal of Remote Sensing, vol. 53, no. 1, pp:104-112, 2020.

  • S. Liu, Q. Hu, X. Tong, J. Xia, Q. Du, A. Samat, X. Ma, “A Multi-scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery,” Remote Sensing, 12, 862, 2020.

  • S. Liu, Q. Du, X. Tong, A. Samat, L. Bruzzone, “Unsupervised Change Detection in Multispectral Remote Sensing Images via Spectral-Spatial Band Expansion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 9, pp:3578-3587, 2019.

  • S. Liu, D. Marinelli, L. Bruzzone and F. Bovolo, "A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges," IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 2, pp. 140-158, 2019.

  • S. Liu, Q. Du, X. Tong, A. Samat, H. Pan , X. Ma, “Band Selection based Dimensionality Reduction for Change Detection in Multitemporal Hyperspectral Images,” Remote Sensing, vol. 9, no.10, pp:1008, 2017.

  • S. Liu, Q. Du, X. Tong, A. Samat, L. Bruzzone, F. Bovolo, “Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 9, pp. 4124-4137, 2017.

  • S. Liu, M. Chi, Y. Zou, A. Samat, J.A. Benediktsson, A. Plaza, “Oil Spill Detection via Multitemporal Optical Remote Sensing Images: A Change Detection Perspective,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no.3, pp:324-328, 2017.

  • S. Liu, L. Bruzzone, F. Bovolo, P. Du., “Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple Changes in Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol.54, no. 5, pp:2733-2748, 2016.

  • S. Liu, L. Bruzzone, F. Bovolo, M. Zanetti, P. Du., “Sequential Spectral Change Vector Analysis for Iteratively Discovering and Detecting Multiple Changes in Hyperspectral Images”, IEEE Transactions on Geoscience and Remote Sensing, vol.53, no. 8, pp:4363-4378, 2015.

  • S. Liu, L. Bruzzone, F. Bovolo, P. Du, “Hierarchical Unsupervised Change Detection in Multitemporal Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol.53, no.1, pp: 244-260, 2015.

    论文列表请参见:Google ScholarResearchgate