蔡耀明

发布者：陈溪发布时间：2023-04-12浏览次数：887


计算机科学与技术系 | 计算机教研室 | 博士 | 准聘制副教授

153-9286-3776 | caiyaom@cug.edu.cn | 中共党员 | 1992-03

Google Scholar: https://scholar.google.com/citations?user=iMWo7sIAAAAJ&hl=zh-CN

ResearchGate:https://www.researchgate.net/profile/Yaoming-Cai

Github: https://github.com/AngryCai   ORCID: 0000-0002-2609-3036

IEEE会员 | ACM会员 | CCF会员 | IEEE GRSS 会员

教育背景

2021.08-2022.08

CSC联合培养博士

德国亥姆霍兹德累斯顿罗森多夫研究中心（HZDR-HIF）



外导：Pedram Ghamis 教授

2018.09-2022.12

博士 (硕博连读)

地学信息工程(计科)，中国地质大学 (武汉)，计算机学院



导师：蔡之华教授

2016.09-2018.06

硕士 (推免)

计算机科学与技术，中国地质大学 (武汉)，计算机学院，



导师：蔡之华教授

2012.09-2016.06

学士

信息安全，中国地质大学 (武汉)，计算机学院

研究方向

  • 多模态遥感图像融合 （Multimodal Remote Sensing Image Fusion）

  • 自监督深度学习 （Self-supervised Deep Learning）

  • 遥感影像智能解译（Remote Sensing Imagery Intelligent interpretation）

  • 图神经网络（Graph Neural Networks）

  • 演化计算 （Evolutionary Learning）

著作

刘小波，蔡之华，蔡耀明，姜鑫维，“智能优化方法及其在高光谱图像处理中的应用,” 武汉：中国地质大学出版社，2021

期刊论文

2023

  1. Y. Cai, Z. Zhang, P. Ghamisi, Z. Cai, X. Liu, and Y. Ding, "Fully Linear Graph Convolutional Networks for Semi-Supervised and Unsupervised Classification," ACM Transactions on Intelligent Systems and Technology, vol. 14, pp. 1-23, 2023. (JCR Q1，IF：10.49）

  2. Z. Zhang, Y. Cai, and W. Gong, "Semi-supervised learning with graph convolutional extreme learning machines," Expert Systems with Applications, vol. 213, p. 119164, 2023. (JCR Q1，中科院1区Top，CCF-C，IF: 8.67)

  3. K. Li, Q. Ling, Y. Wang, Y. Cai, Y. Qin, Z. Lin, et al., "Spectral Difference Guided Graph Attention Autoencoder for Hyperspectral Anomaly Detection," IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-17, 2023. (JCR Q1，中科院2区，IF: 5.33)

  4. Z. Zhang, Y. Ding, X. Zhao, L. Siye, N. Yang, Y. Cai, et al., "Multireceptive field: An adaptive path aggregation graph neural framework for hyperspectral image classification," Expert Systems with Applications, vol. 217, p. 119508, 2023. (JCR Q1，中科院1区Top，CCF-C，IF: 8.67)



2022

  1. Y. Cai, Z. Zhang, P. Ghamisi, Y. Ding, X. Liu, Z. Cai, and R. Gloaguen, “Superpixel Contracted Neighborhood Contrastive Subspace Clustering Network for Hyperspectral Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022.(JCR Q1，中科院1区Top，CCF-B，IF: 8.13)

  2. T. Li, Y. Cai, Y. Zhang, Z. Cai and X. Liu, "Deep Mutual Information Subspace Clustering Network for Hyperspectral Images," IEEE Geoscience and Remote Sensing Letters, 2022. (JCR Q1，CCF-C，中科2区，IF：5.34)

  3. Y. Cai, Z. Zhang, P. Ghamisi, Y. Ding, X. Liu, Z. Cai, et al., "Superpixel contracted neighborhood contrastive subspace clustering network for hyperspectral images," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022. (JCR Q1，中科院1区Top，CCF-B，IF: 8.13)

  4. Y. Ding, Z. Zhang, X. Zhao, Y. Cai, S. Li, B. Deng, et al., "Self-supervised locality preserving low-pass graph convolutional embedding for large-scale hyperspectral image clustering," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022. (JCR Q1，中科院1区Top，CCF-B，IF: 8.13，🏆ESI高被引，热点论文)

  5. K. Li, Q. Ling, Y. Qin, Y. Wang, Y. Cai, Z. Lin, et al., "Spectral-spatial deep support vector data description for hyperspectral anomaly detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022. (JCR Q1，中科院1区Top，CCF-B，IF: 8.13)

  6. T. Li, Y. Cai, Y. Zhang, Z. Cai, and X. Liu, "Deep mutual information subspace clustering network for hyperspectral images," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022. (JCR Q1，CCF-C，中科2区，IF：5.34)

  7. Z. Zhang, Y. Cai, and W. Gong, "Evolution-driven randomized graph convolutional networks," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, pp. 7516-7526, 2022. (JCR Q1，中科院1区Top，CCF-B，IF: 11.47)

  8. Q. Yan, N. Wang, X. Jiang, Y. Cai, Y. Zhang, X. Liu, et al., "Classification of hyperspectral images using a propagation filter and convolutional neural network," Remote Sensing Letters, vol. 13, pp. 429-440, 2022.

  9. D. Yao, Z. Zhi-li, Z. Xiao-feng, C. Wei, H. Fang, Y. Cai, et al., "Deep hybrid: multi-graph neural network collaboration for hyperspectral image classification," Defence Technology, 2022.



2021

  1. Y. Cai, M. Zeng, Z. Cai, X. Liu, and Z. Zhang, "Graph regularized residual subspace clustering network for hyperspectral image clustering," Information Sciences, vol. 578, pp. 85-101, 2021. (JCR Q1，CCF-B，中科院1区Top，IF：8.23)

  2. Y. Cai, Z. Zhang, Z. Cai, X. Liu, and X. Jiang, "Hypergraph-structured autoencoder for unsupervised and semisupervised classification of hyperspectral image," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021. (JCR Q1，CCF-C，中科2区，IF：5.34)

  3. Y. Cai, Z. Zhang, Q. Yan, D. Zhang, and M. J. Banu, "Densely connected convolutional extreme learning machine for hyperspectral image classification," Neurocomputing, vol. 434, pp. 21-32, 2021. (JCR Q1，CCF-C，中科院2区Top，IF：5.78)

  4. T. Li, Y. Cai, Z. Cai, X. Liu, and Q. Hu, "Nonlocal band attention network for hyperspectral image band selection," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 3462-3474, 2021. (JCR Q2，中科院2区，IF：4.72)

  5. Z. Zhang, Y. Cai, W. Gong, P. Ghamisi, X. Liu, and R. Gloaguen, "Hypergraph convolutional subspace clustering with multihop aggregation for hyperspectral image," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 676-686, 2021. (JCR Q2，中科院2区，IF：4.72)



2020

  1. Y. Cai, Z. Zhang, Z. Cai, X. Liu, X. Jiang, and Q. Yan, "Graph convolutional subspace clustering: A robust subspace clustering framework for hyperspectral image," IEEE Transactions on Geoscience and Remote Sensing, vol. 59, pp. 4191-4202, 2020. (JCR Q1，中科院1区Top，CCF-B，IF: 8.13，🏆ESI高被引)

  2. Y. Cai, Z. Zhang, X. Liu, and Z. Cai, "Efficient graph convolutional self-representation for band selection of hyperspectral image," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 4869-4880, 2020. (JCR Q2，中科院2区，IF：4.72)

  3. Z. Dong, Y. Cai, Z. Cai, X. Liu, Z. Yang, and M. Zhuge, "Cooperative spectral–spatial attention dense network for hyperspectral image classification," IEEE Geoscience and Remote Sensing Letters, vol. 18, pp. 866-870, 2020. (JCR Q1，CCF-C，中科2区，IF：5.34)

  4. X. Liu, Q. Hu, Y. Cai, and Z. Cai, "Extreme learning machine-based ensemble transfer learning for hyperspectral image classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3892-3902, 2020.   (JCR Q2，中科院2区，IF：4.72)

  5. X. Fang, Y. Cai, Z. Cai, X. Jiang, and Z. Chen, "Sparse feature learning of hyperspectral imagery via multiobjective-based extreme learning machine," Sensors, vol. 20, p. 1262, 2020.

  6. Z. Zhang, Y. Cai, and D. Zhang, "Solving ordinary differential equations with adaptive differential evolution," IEEE Access, vol. 8, pp. 128908-128922, 2020.



2019

  1. Y. Cai, X. Liu, and Z. Cai, "BS-Nets: An end-to-end framework for band selection of hyperspectral image," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, pp. 1969-1984, 2019. (JCR Q1，中科院1区Top，CCF-B，IF: 8.13，🏆ESI高被引)

  2. X. Liu, R. Wang, Z. Cai, Y. Cai, and X. Yin, "Deep multigrained cascade forest for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 8169-8183, 2019. (JCR Q1，中科院1区Top，CCF-B，IF: 8.13)

  3. X. Liu, X. Yin, Y. Cai, M. Wang, Z. Cai, and B. Huang, "Visual saliency-based extended morphological profiles for unsupervised feature learning of hyperspectral images," IEEE Geoscience and Remote Sensing Letters, vol. 17, pp. 1963-1967, 2019. (JCR Q1，CCF-C，中科2区，IF：5.34)

  4. M. Zeng, Y. Cai, Z. Cai, X. Liu, P. Hu, and J. Ku, "Unsupervised hyperspectral image band selection based on deep subspace clustering," IEEE Geoscience and Remote Sensing Letters, vol. 16, pp. 1889-1893, 2019. (JCR Q1，CCF-C，中科2区，IF：5.34)

  5. P. Hu, X. Liu, Y. Cai, and Z. Cai, "Band Selection of Hyperspectral Images Using Multiobjective Optimization-Based Sparse Self-Representation," IEEE Geoscience and Remote Sensing Letters, vol. 16, pp. 452-456, 2019. (JCR Q1，CCF-C，中科2区，IF：5.34)

  6. X. Liu, X. Yin, M. Wang, Y. Cai, and G. Qi, "Emotion recognition based on multi-composition deep forest and transferred convolutional neural network," Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 23, pp. 883-890, 2019.



2018

  1. Y. Wu, Y. Zhang, X. Liu, Z. Cai, and Y. Cai, "A multiobjective optimization-based sparse extreme learning machine algorithm," Neurocomputing, vol. 317, pp. 88-100, 2018.(JCR Q1，CCF-C，中科2区，IF：5.78)

  2. Y. Cai, X. Liu, Y. Zhang, and Z. Cai, "Hierarchical ensemble of Extreme Learning Machine," Pattern Recognition Letters, vol. 116, pp. 101-106, 2018. (JCR Q2，CCF C，中科院3区，IF：4.75)



会议论文

  1. Y. Cai, Z. Cai, M. Zeng, X. Liu, J. Wu, and G. Wang, "A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder," in 2018 International Joint Conference on Neural Networks (IJCNN), 2018, pp. 1-8. (Oral, CCF C)

  2. Y Cai, Z Zhang, Y Liu, P Ghamisi, K Li, X Liu, Z Cai, "Large-Scale Hyperspectral Image Clustering Using Contrastive Learning." CIKM 2021 Workshop

  3. Y. Cai, X. Liu, Y. Wu, P. Hu, R. Wang, B. Wu, et al., "Extreme Learning Machine Based on Evolutionary Multi-objective Optimization," in 2017 12th Bio-inspired Computing: Theories and Applications (BIC-TA), 2017, pp. 420-435. （Oral）

  4. Y. Cai, Z. Dong, Z. Cai, X. Liu, and G. Wang, "Discriminative Spectral-Spatial Attention-Aware Residual Network For Hyperspectral Image Classification," in 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019, pp. 1-5.

  5. Z. Zhang, Y. Cai, W. Gong, X. Liu, and Z. Cai, "Graph Convolutional Extreme Learning Machine," in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1-8. (Oral, CCF C)

  6. M. Zeng, Y. Cai, X. Liu, Z. Cai, and X. Li, "Spectral-Spatial Clustering of Hyperspectral Image Based on Laplacian Regularized Deep Subspace Clustering," in IGARSS, 2019, pp. 2694-2697.

研究项目

2019-2021

基于自监督深度学习方法的高光谱图像分析（1910491T06）

中央高校基本科研业务费项目

项目负责人

2018-2021

基于演化算法和超限学习机的高光谱遥感图像信息处理研究（61773355）

NSFC面上项目

参与

2019-2022

面向高光谱图像空谱信息提取的深度神经网络优化方法 （61973285）

NSFC面上项目

参与

2016-2019

基于混合差分演化算法及集成迁移学习的高光谱遥感图像分类方法研究（61603355）

NSFC青年基金

参与

2018-2019

智能优化算法及其在高光谱遥感图像处理中的应用

开放基金

参与

发明专利

  1. 蔡耀明,张子佳,刘小波,蔡之华,刘哲伟,王梦琪,邓雅雯. 一种基于深度学习的高光谱图像波段选择方法[P]. 湖北省：CN111191514A,2020-05-22.

  2. 蔡耀明,李天聪,张子佳,曾梦,蔡之华,刘小波,董志敏. 一种基于残差子空间聚类网络的高光谱图像聚类方法[P]. 湖北省：CN111144463A,2020-05-12.

  3. 张子佳,蔡耀明,龚文引,刘小波,蔡之华. 一种基于图卷积极限学习机的快速半监督分类方法[P]. 湖北省：CN111626332B,2021-03-30. （授权）

  4. 刘小波,尹旭,蔡耀明,王瑞林. 一种基于深度森林和迁移学习的情感分类方法[P]. 湖北省：CN109389037B,2021-05-11. （授权）

  5. 刘小波,尹旭,王瑞林,蔡耀明,刘振焘. 一种高光谱遥感图像的分类方法、设备及存储设备[P]. 湖北省：CN108614992B,2021-07-20.（授权）

  6. 刘小波,尹旭,刘沛宏,汪敏,蔡耀明,乔禹霖,刘鹏. 基于注意力机制和卷积神经网络高光谱遥感图像分类方法[P]. 湖北省：CN109376804B,2020-10-30. （授权）

  7. 董志敏,蔡之华,蔡耀明,龚赛,刘小波,尹旭. 基于并行注意力机制残差网进行高光谱图像分类的方法[P]. 湖北省：CN111274869A,2020-06-12.

  8. 刘小波,尹旭,汪敏,蔡耀明,张超超,周志浪. 基于视觉显著性的高光谱遥感图像分类方法及系统[P]. 湖北省：CN110458192A,2019-11-15.

荣誉奖项

2021-2022

博士研究生国家奖学金

中华人民共和国教育部

2020-2021

博士研究生国家奖学金

中华人民共和国教育部

2021-2022

国家建设高水平大学公派研究生项目奖学金

国家留学基金委（CSC）

2020

“华为杯”中国研究生数学建模竞赛 全国三等奖

教育部学位与研究生教育发展中心

2020

第四届中国计算机学会武汉优博士论坛  二等奖

中国计算机学会

2018-2022

博士研究生一等学业奖学金

中国地质大学（武汉）

2016-2018

硕士研究生一等学业奖学金

中国地质大学（武汉）

2016

湖北省优秀学士学位论文

湖北省教育厅

2015

第八届全国老员工信息安全竞赛  全国二等奖

教育部信息安全专业指导委员会

2014

全国高校移动互联网应用创新大赛  全国一等奖

教育部科技发展中心

社会服务

兼职审稿：IEEE Transactions on Geoscience and Remote Sensing

ISPRS Journal of Photogrammetry and Remote Sensing

Information Sciences

Expert Systems with Applications

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

IEEE Geoscience and Remote Sensing Letters

Geocarto International

International Journal of Machine Learning and Cybernetics

ICML

NeurIPS

IJCNN

Whispers

CCC

CDCEO



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