赵济

基本信息Personal Information

副教授 硕士生导师

性别 : 男

毕业院校 : 武汉大学

学历 : 博士研究生

学位 : 工学博士学位

所在单位 : 计算机学院

入职时间 : 2017年07月01日

联系方式 : zhaoji##cug.edu.cn(用@替换##)

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个人简介Personal Profile

赵济,现任中国地质大学(武汉)计算机学院副教授,硕士生导师。2017年毕业于武汉大学测绘遥感信息工程国家重点实验室,获摄影测量与遥感专业工学博士学位,德国航空航天中心博士后。

现主要从事机器学习与深度学习等人工智能算法、计算机视觉、图像处理以及遥感影像地学应用等方面的研究工作。在RSE、ISPRS、TIP、TGRS等国际权威期刊和会议发表学术论文40余篇,引用量700余次,其中一区、二区SCI论文20余篇,ESI全球1%高被引论文1篇、ESI全球0.1%热点论文1篇。

近几年主持国家自然科学面上基金、国家自然科学青年基金、“地大学者”青年优秀人才项目、测绘遥感信息工程国家重点实验室开放基金、湖北省智能地学信息处理重点实验室开放基金等项目,并作为研究骨干参与国家发改委项目、广东省国土资源技术中心合作项目等重点科研项目共十余项。获John I. Davidson总统奖、测绘科技进步奖一等奖、地理信息科技进步奖二等奖、IEEE数据融合与分类大赛亚军等奖励,担任 International Journal of Computer Vision (IJCV)、IEEE Transactions on Geoscience and Remote Sensing (TGRS)、ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS)、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)、International Journal of Remote Sensing (IJRS) 等计算机/遥感领域国际权威期刊审稿员。

研究方向

    主要研究方向为人工智能、计算机视觉和图像处理。具体研究内容包括:

  l  迁移学习、深度学习与机器学习算法

  l  目标探测与识别、语义分割与分类、图像变化检测

  l  高维数据信息挖掘、图像智能处理与解译

  l  大数据信息综合与决策

热烈欢迎具有计算机科学与技术/遥感/测绘/图像处理等相关背景的同学交流学习和报考!

课题组以优秀科研成果(展现形式为文章、算法、专利等)为目标,希望你具有积极认真的学习态度,创新诚实的科研精神!本课题组将为每位同学提供良好的科研氛围和高水准的科研指导与支持,积极引导每位研究生不断遇见更好的自己,寻求更好的发展!

联系方式:zhaoji##cug.edu.cn(将##替换成@)

获奖经历

  l  John I. Davidson总统奖.2021

  l  中国地质大学(武汉)本科毕业设计优秀指导教师.2019

  l  国土资源(广东)科学技术一等奖.2019

  l  测绘科技进步奖一等奖.2018

  l  地理信息科技进步奖二等奖.2018

  l  IEEE数据融合与分类大赛亚军.2013

科研基金项目

  l  国家自然科学基金面上项目:基于多尺度深度网络的高光谱影像异质作物精细分类方法研究,时间:2022.01.01-2025.12.31,主持

  l  国家自然科学基金青年项目:空间细节保持的高光谱遥感影像条件随机场分类方法研究,时间:2019.01.01-2021.12.31,主持

  l  测绘遥感信息工程国家重点实验室开放基金项目:基于条件随机场的无人机高光谱遥感影像农作物精细分类方法研究,时间:2018.01.01-2019.12.31,主持

  l  国防横向:多源遥感影像**系统开发,时间:2017.12-2018.6,主持

  l  中国地质大学(武汉)杰出人才培育基金项目:基于条件随机场的高分辨率遥感影像分类方法研究,主持

  l  国家自然科学基金面上项目:基于稀疏概率图模型的高分辨率遥感影像场景语义理解方法研究,时间:2018.01-2021.12;担任研究骨干,主要承担概率图模型研究以及高层语义深度理解,揭示场景内所蕴含的关键目标及目标间的空间语义关系。

  l  国家自然科学基金优秀青年科学基金项目:高光谱遥感地物识别与场景理解;时间:2017.01-2019.12;担任研究骨干,主要承担高空间分辨率的高光谱遥感影像的地物识别研究。

  l  国家自然科学基金面上项目:空-谱融合高光谱遥感影像混合像元稀疏分解与空间定位,时间:2014.01-2017.12;担任技术骨干,主要承担高光谱遥感影像亚像元定位算法研究。

  l  湖北省自然科学基金杰出青年科学基金项目:高分辨率遥感影像的多特征场景语义理解,时间:2017.01-2019.12;担任研究骨干,主要承担高分辨率遥感影像目标场景一体化识别的研究。

  l  国家发改委项目:“基于高分辨卫星影像的全球测图技术系统及应用”子项目“地表覆盖分类子系统”,担任技术骨干,主要承担底层框架、特征分类算法的开发以及系统开发。

  l  广东省国土资源技术中心合作项目:基于卫片等遥感影像的改造项目进度监测技术研究,时间:2015.01-2015.06;担任技术骨干,研究高分遥感影像监督变化检测应用于城区改造工程的监测,完成特定区域的技术验证实验。

发明专利

  l  赵济,李才勇,董宇婷,周明,王力哲.一种基于数字高程模型的冰川崩解前沿自动提取方法[P]中国专利:CN112926408A. 2021-06-08

  l  赵济,王力哲,王为琼,董宇婷.一种顾及光谱重要性的高光谱影像空谱分类方法及装置[P]中国专利:CN110796163A.2020-02-14

  l  赵济,王力哲,王为琼,董宇婷.一种面向分类的高光谱遥感影像噪声波段探测方法[P]中国专利:CN110443139A.2019-11-12

  l  钟燕飞,赵济,吕鹏远,王晶,马爱龙,刘艳飞,伍丝琪,张良培. 基于并行算法的高分辨率遥感影像的地表覆盖分类方法[P]中国专利:CN107909039A.2018-04-13

代表性学术论文(标注:学生

[1]  J. Zhao, Y. Zhong, X. Hu, L. Wei, and L. Zhang, "A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions," Remote Sensing of Environment, vol. 239, p. 111605, 2020. (SCI, T1)

[2]  J. Zhao, Y. Zhong, T. Jia, X. Wang, Y. Xu, H. Shu, and L. Zhang, "Spectral-spatial classification of hyperspectral imagery with cooperative game," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 135, pp. 31-42, 2018. (SCI, T1)

[3]  J. Zhao, S. Tian, C. Geiß, L. Wang, Y. Zhong, and H. Taubenböck, "Spectral-Spatial Classification Integrating Band Selection for Hyperspectral Imagery With Severe Noise Bands," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 1597-1609, 2020. (SCI, T2)

[4]  J. Zhao, Y. Zhong, H. Shu, and L. Zhang, "High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields," IEEE Transactions on Image Processing, vol. 25, pp. 4033-4045, Sept. 2016. (SCI, T2, CCF A)

[5] J. Zhao, Y. Zhong, and L. Zhang, "Detail-Preserving Smoothing Classifier Based on Conditional Random Fields for High Spatial Resolution Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, pp. 2440-2452, May 2015. (SCI, T2)

[6]  J. Zhao, Y. Zhong, Y. Wu, L. Zhang, and H. Shu, "Sub-Pixel Mapping Based on Conditional Random Fields for Hyperspectral Remote Sensing Imagery," IEEE Journal of Selected Topics in Signal Processing, vol. 9, pp. 1049-1060, Sept. 2015. (SCI, T2)

[7]  Y. Su, Y. Zhong, Q. Zhu, and J. Zhao, "Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 179, pp. 50-65, 2021. (SCI, T1)

[8]  X. Hu, Y. Zhong, X. Wang, C. Luo, J. Zhao, L. Lei, and L. Zhang, "SPNet: Spectral Patching End-to-End Classification Network for UAV-Borne Hyperspectral Imagery With High Spatial and Spectral Resolutions," IEEE Transactions on Geoscience and Remote Sensing, pp. 1-17, 2021. (SCI, T2)

[9]  Y. Dong, J. Zhao, D. Floricioiu, L. Krieger, T. Fritz, and M. Eineder, "High-resolution topography of the Antarctic Peninsula combining the TanDEM-X DEM and Reference Elevation Model of Antarctica (REMA) mosaic," The Cryosphere, vol. 15, pp. 4421-4443, 2021. (SCI, T1)

[10] Y. Zhong, J. Wang, and J. Zhao, "Adaptive conditional random field classification framework based on spatial homogeneity for high-resolution remote sensing imagery," Remote Sensing Letters, vol. 11, pp. 515-524, 2020. (SCI, T3)

[11] Y. Zhong, Y. Su, S. Wu, Z. Zheng, J. Zhao, A. Ma, Q. Zhu, R. Ye, X. Li, and P. Pellikka, "Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities," Remote Sensing of Environment, vol. 247, p. 111838, 2020. (SCI, T1)

[12] Y. Zhong, X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang, "WHU-Hi: UAV-borne hyperspdectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF," Remote Sensing of Environment, vol. 250, p. 112012, 2020. (SCI, T1)

[13] Z. Zheng, Y. Zhong, A. Ma, X. Han, J. Zhao, Y. Liu, and L. Zhang, "HyNet: Hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 166, pp. 1-14, 2020. (SCI, T1)

[14] S. Wang, Y. Zhong, J. Zhao, X. Wang, and X. Hu, "S 3 CRF: Sparse Spatial-Spectral Conditional Random Field Target Detection Framework for Airborne Hyperspectral Data," IEEE Access, vol. 8, pp. 46917-46930, 2020. (SCI, T2)

[15] S. Shi, Y. Zhong, J. Zhao, P. Lv, Y. Liu, and L. Zhang, "Land-Use/Land-Cover Change Detection Based on Class-Prior Object-Oriented Conditional Random Field Framework for High Spatial Resolution Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, 2020. (SCI, T2)

[16] X. Lu, Y. Zhong, Z. Zheng, J. Zhao, and L. Zhang, "Edge-Reinforced Convolutional Neural Network for Road Detection in Very-High-Resolution Remote Sensing Imagery," Photogrammetric Engineering & Remote Sensing, vol. 86, pp. 153-160, 2020. (SCI, T3)

[17] Y. Dong, L. Krieger, D. Floricioiu, and J. Zhao, "Glacier calving front extraction from TanDEM-X DEM products of the Antarctic Peninsula," in EGU General Assembly Conference Abstracts, 2020, p. 20137. (EI)

[18] 曹琼, 马爱龙, 钟燕飞, 赵济, 赵贝, and 张良培, "高光谱-LiDAR 多级融合城区地表覆盖分类," 遥感学报, 2019. (EI)

[19] N. Zhao, A. Ma, Y. Zhong, J. Zhao, and L. Cao, "Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones," Remote Sensing, vol. 11, p. 2828, 2019. (SCI, T2)

[20] L. Wei, M. Yu, Y. Zhong, J. Zhao, Y. Liang, and X. Hu, "Spatial–Spectral Fusion Based on Conditional Random Fields for the Fine Classification of Crops in UAV-Borne Hyperspectral Remote Sensing Imagery," Remote Sensing, vol. 11, p. 780, 2019. (SCI, T2)

[21] X. Lu, Y. Zhong, Z. Zheng, Y. Liu, J. Zhao, A. Ma, and J. Yang, "Multi-scale and multi-task deep learning framework for automatic road extraction," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 9362-9377, 2019. (SCI, T2)

[22] X. Lu, Y. Zhong, and J. Zhao, "Multi-Scale Enhanced Deep Network for Road Detection," in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 3947-3950. (EI)

[23] X. Hu, X. Wang, Y. Zhong, J. Zhao, C. Luo, and L. Wei, "SPNet: A Spectral Patching Network for End-To-End Hyperspectral Image Classification," in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 963-966. (EI)

[24] Y. Zhong, X. Wang, Y. Xu, S. Wang, T. Jia, X. Hu, J. Zhao, L. Wei, and L. Zhang, "Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications," IEEE Geoscience and Remote Sensing Magazine, vol. 6, pp. 46-62, 2018. (SCI, T2)

[25] Y. Zhong, R. Huang, J. Zhao, B. Zhao, and T. Liu, "Aurora image classification based on multi-feature latent dirichlet allocation," Remote Sensing, vol. 10, p. 233, 2018. (SCI, T2)

[26] P. Lv, Y. Zhong, J. Zhao, and L. Zhang, "Unsupervised Change Detection Based on Hybrid Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, pp. 4002-4015, 2018. (SCI, T2)

[27] P. Lv, Y. Zhong, J. Zhao, and L. Zhang, "Change detection based on conditional random field model with spectral-spatial prior information for high spatial resolution remote sensing imagery," Nanjing Xinxi Gongcheng Daxue Xuebao, vol. 10, pp. 123-130, 2018. (EI)

[28] Y. Dong, B. Liu, L. Zhang, M. Liao, and J. Zhao, "Fusion of Multi-Baseline and Multi-Orbit InSAR DEMs with Terrain Feature-Guided Filter," Remote Sensing, vol. 10, p. 1511, 2018. (SCI, T2)

[29] Y. Zhong, T. Jia, J. Zhao, X. Wang, and S. Jin, "Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery," Remote Sensing, vol. 9, p. 910, 2017. (SCI, T2)

[30] Y. Zhong, Q. Cao, J. Zhao, A. Ma, B. Zhao, and L. Zhang, "Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data," Remote Sensing, vol. 9, p. 868, 2017. (SCI, T2)

[31] P. Lv, Y. Zhong, J. Zhao, A. Ma, and L. Zhang, "Change detection based on structural conditional random field framework for high spatial resolution remote sensing imagery," in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 1059-1062. (EI)

[32] Y. Liu, Y. Zhong, J. Zhao, A. Ma, and Q. Qin, "Scene semantic classification based on scale invariance convolutional neural networks," in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 4754-4757. (EI)

[33] J. Zhao, Y. Zhong, R. Gao, L. Zhang, and H. Shu, "Feature extraction framework in class space for hyperspectral image classification," in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp. 3164-3167. (EI)

[34] P. Lv, Y. Zhong, J. Zhao, and L. Zhang, "Unsupervised change detection model based on hybrid conditional random field for high spatial resolution remote sensing imagery," in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp. 1863-1866. (EI)

[35] P. Lv, Y. Zhong, J. Zhao, H. Jiao, and L. Zhang, "Change Detection Based on a Multifeature Probabilistic Ensemble Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery," IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 1965-1969, 2016. (SCI, T3)

[36] Y. Zhong, W. Liu, J. Zhao, and L. Zhang, "Change Detection Based on Pulse-Coupled Neural Networks and the NMI Feature for High Spatial Resolution Remote Sensing Imagery," IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 537-541, 2015. (SCI, T3)

[37] J. Zhao, Y. Zhong, H. Shu, and L. Zhang, "Spectral-spatial conditional random field classifier with location cues for high spatial resolution imagery," in Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, 2015, pp. 4380-4383. (EI)

[38] P. Lv, Y. Zhong, J. Zhao, and L. Zhang, "Unsupervised change detection based on conditional random fields and texture feature for high resolution remote sensing imagery," in Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, 2015, pp. 1081-1085. (EI)

[39] Y. Zhong, J. Zhao, and L. Zhang, "A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, pp. 7023-7037, Nov. 2014. (SCI, T2)

主讲课程

  l  《Java程序设计》,本科,2018年春/2019年春/2021年春

  l  《Java程序设计A》,本科,2019年秋

  l  《Java程序设计B》,本科,2019年春

  l  《C语言程序设计》,本科,2018年春/2018年秋/2019年秋/2021年秋

  l  《计算机前沿介绍》,本科,2018年春/2019年春/2020年春

  l  《图像处理与分析》,本科,2021年秋

  l  《地学信息工程学科前沿》,博士,2018年秋/2020年秋

学术兼职

  担任遥感、计算机领域国际权威期刊审稿员:

  l  International Journal of Computer Vision

  l  IEEE Transactions on Image Processing

  l  Remote Sensing of Environment

  l  ISPRS Journal of Photogrammetry and Remote Sensing

  l  IEEE Transaction on Geoscience and Remote Sensing

  l  Neurocomputing

  l  IEEE Geoscience and Remote Sensing Letter

  l  Remote Sensing

  l  International Journal of Remote Sensing

教育经历

  l  2019.11 -- 2020.11

    德国航空航天中心(DLR) >  博士后

  l  2012.9 -- 2017.7

    武汉大学 > 测绘遥感信息工程国家重点实验室 > 工学博士学位


工作经历

  l  2017.7 -- 至今

    中国地质大学(武汉) >  计算机学院  >  副教授

  • 教育经历Education Background
  • 工作经历Work Experience
  • 研究方向Research Focus
  • 社会兼职Social Affiliations