学院概况

赵林，湖南湘潭人，博士，教授，硕士生导师，Fox++（湖理智能）实验室负责人。2007年毕业于华中科技大学计算机学院获硕士学位，2017年毕业于中南大学信息科学与工程学院获博士学位。现为湖南理工学院专职教师，信息与通信工程专业学术型硕士生导师、电子信息和机械工程专业（计算机视觉领域）专业型硕士生导师，主要从事人工智能、计算机视觉以及智能控制等领域的研究工作。

主持国家自然科学基金项目1项、湖南省自然科学基金项目2项、湖南省教育厅科学研究重点项目、优秀青年项目各1项，主持湖南省教育厅教改项目2项，参与国家级自然科学基金项目两项。在国内外重要期刊和国际会议发表论文40余篇，其中SCI/EI收录30多篇，入选ESI全球Top1%高被引论文2篇。已取得授权国家发明专利4项，同时获得了法国巴黎举办的第9届IFAC工艺过程故障检测、监控和安全专题讨论会（the 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes）最佳论文提名奖，并兼任多个国际期刊审稿人。

创建了FOX++（湖理智能）实验室，探索以实践创新为导向，以学科竞赛为抓手的实践创新人才培养新路径，实验室学生获得省级以上奖励近100项。以第一指导老师身份指导研究生参加中国研究生数学建模竞赛，获得国家级一等奖1项、二等奖6项，三等奖2项，荣获第十九届中国研究生数学建模竞赛先进个人（学院命名高校中唯一先进个人）；指导研究生参加湖南省研究生数学建模竞赛，获得省级一等奖2项，二等奖7项、三等奖2项，荣获2023年湖南省研究生数学竞赛优秀指导老师。指导的研究生获得2024年湖南省人工智能创新大赛一等奖，学校首次获得该赛事的一等奖，获得2024年湖南省人工智能创新大赛优秀指导老师。截止目前共指导硕士研究生15人，已毕业的5名研究生中，均在科研院所（西湖大学光学研究所、中南大学、深圳大学）和高新技术企业从事技术研发工作，获评2022年度、2023年度和2024年度湖南理工学院“优秀研究生指导老师”。

同时以第一指导老师身份指导实验室本科生参加全国大学生数学建模竞赛，获得国家二等奖2项、湖南省一等奖2项、二等奖3项、三等奖2项，荣获2021年、2022年湖南省大学生数学建模竞赛优秀指导老师；指导本科生参加湖南省“互联网+”大学生创新创业大赛、“挑战杯”湖南省大学生创业计划竞赛分获省一、二等奖和银奖，荣获2023年度湖南省“互联网+”大学生创新创业大赛优秀创新创业导师。

1. 研究方向：

2. 计算机视觉

3. 人工智能算法

4. 智能控制

5. 主持或参加的科研项目：

   主持国家自然科学基金1项，湖南省自然科学基金2项、湖南省教育厅优秀青年科研项目、重点项目各1项、湖南省教育厅教改项目2项，参与国家级自然科学基金项目5项。主持研发横向项目多项，有丰富的机器视觉和复杂工业过程智能控制领域项目开发经验。

6. 教学：

   《机器人及其控制》

   《神经网络与深度学习》

   《强化学习》

   《数字图像处理》

   《自动控制工程案例》

7. 部分代表性论文:

8. Jiahong Jin, Dujie Liao, Lin Zhao*(通信作者), Marion S. Greene, Yu Sa, Heng Hong, Xin-Hua Hu*. Accurate Classification of Human CD4+ T, CD8+ T, and CD19+ B Cells Isolated from Splenocytes by Cross-Polarized Diffraction Image Pairs. Analytical Chemistry, 2025, 97(3): 1603-1611. (中科院 SCI一区Top)

9. Jia Li, Lin Zhao*(通信作者), Yuanjie Dai, Minhui Zhao, Minhao Li and Jianhui Wu. Collaborative Spectral–Spatial Representation Learning for Hyperspectral and LiDAR Classification Under Limited Samples, IEEE Geoscience and Remote Sensing Letters, 2025, 22: 1-5, 2025, Art no. 2502205, doi: 10.1109/LGRS.2025.3559913.

10. Yuanjie Dai , Jianhui Wu , Tian Luo, Jia Li , Shaoxiong Xie, and Lin Zhao*(通信作者). Boosting Multimodal Remote Sensing Image Classification via Prompt-Driven Fusion, IEEE Geoscience and Remote Sensing Letters, 2025, 22: 1-5, 2025, Art no. 5503505, doi: 10.1109/LGRS.2025.3554798.

11. Guoyun Zhang, Ping Tan, Xiaoqi Fang, Xuhui Zhou and Lin Zhao*(通信作者). A Unified Self-Supervised Learning Framework for Hyperspectral Image Classification, IEEE Access, 2025, 13:49874-49890, doi: 10.1109/ACCESS.2025.3549277.

12. Li J. Lin Zhao*(通信作者), Dai Y, et al. A Leader-Follower Contrastive Learning Framework for Imbalanced Multimodal Remote Sensing Image Classification[J]. International Journal of Remote Sensing, 2025.

13. Lin Zhao, Youlin Zhang, Chengzhong Shi, Minhui Zhao, Jianhui Wu and Wen Li*. APNet: A Novel Anti-Perturbation Network for Robust Hyperspectral Image Classifcation against Adversarial Attacks. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-14,  Art no. 5534614. DOI: 10.1109/TGRS.2024.3467088. (中科院 SCI一区Top)

14. Lin Zhao, Jia Li, Wenqiang Luo, Er Ouyang, Jianhui Wu, Guoyun Zhang, Wujin Li. Purified Contrastive Learning With Global and Local Representation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-14, Art no. 5520414 (中科院 SCI一区Top)

15. Qiming Liao, Lin Zhao(共同一作), Wenqiang Luo, Xinping Li, Guoyun Zhang. Joint negative-positive-learning based sample reweighting for hyperspectral image classification with label noise. Pattern Recognition Letters, 2024, 183: 98-103

16. Zhao L, Feng Y, Dai Y J, Wu J H*, Zhang G Y*. Progressive Contrastive Learning Based On Noisy Negatives Cleaning for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2024, 21:1-5, Art no. 5504205

17. Fang C H, Zhang G F, Li J, Li X P, Chen T F, Zhao L*(通信作者). Intelligent Agent for Hyperspectral Image Classification with Noisy Labels: A Deep Reinforcement Learning Framework. International Journal of Remote Sensing, 2024, 45(9), 2939-2964

18. Ouyang E, Li B, Hu W, Zhang G, Zhao L*(通信作者), Wu J. When Multi-Granularity Meets Spatial-Spectral Attention: A Hybrid Transformer for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61:1-18, Art no. 4401118 (中科院 SCI一区Top)

19. Zhao L, Zhou X, Lu X, Tong H, Fang H. Transformer-based reconstruction for Fourier ptychographic microscopy, IEEE Access, 2023, 11: 94536-94544

20. He J, Zhao L(共同一作), Hu W, Zhang G, Wu J, Li X. TCM-Net: Mixed Global–Local Learning for Salient Object Detection in Optical Remote Sensing Images. Remote Sensing. 2023; 15(20):4977

21. Shi C, Liao Q, Li X, Zhao L*(通信作者), Li W. Graph Guided Transformer: An Image-Based Global Learning Framework for Hyperspectral Image Classification[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20:1-5

22. Zhao L, Ouyang E, Tang J, Li B, Wu J, Zhang G. Domain transfer and difference-aware band weighting for object tracking in hyperspectral videos. International Journal of Remote Sensing, 2023, 44(4): 1115-1131

23. 吴健辉,何灿,何俊康,谢永芳,赵林(通信作者),张国云. FSNet:基于频率特性的烟雾图像分割网络. 控制理论与应用, 2023,40(04):702-712 (EI)

24. Zhao L, Tang L, Greene M S, Sa Y, Wang W, Jin J, ... Hu X. Deep Learning of Morphologic Correlations to Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow Cytometry. Analytical Chemistry, 2022, 94(3), 1567–1574 (中科院SCI一区Top)

25. Zhao L, Luo W, Liao Q, Chen S, Wu J. Hyperspectral Image Classification with Contrastive Self-Supervised Learning Under Limited Labeled Samples. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5, Art no.6008205

26. Ouyang E, Wu J, Li B, Zhao L(通信作者), Hu W. Band Regrouping and Response-Level Fusion for End-to-End Hyperspectral Object Tracking. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5, Art no.6005805

27. Li B, Ouyang E, Hu W, Zhang G, Zhao L(通信作者), Wu J. Multi-granularity Vision Transformer Via Semantic token for Hyperspectral Image Classification. International Journal of Remote Sensing, 2022, 43(17): 6538-6560

28. 赵林,李希,谢永芳,易嘉闻,吴健辉,胡文静. 基于自适应变量加权的汽油辛烷值预测方法. 控制与决策, 2022, 37(10): 2738-2744 (EI)

29. Liu L, Zhou X, Liao Q, Liao Q, Hu W, Zhao L(通信作者). Burst bubble recognition based on depth feature. In Proceedings of the 4th International Conference on Informatics Engineering & Information Science(ICIEIS2021), 12161:10-14, 2022

30. Zhao L, Yi J, Li X, Hu W, Wu J, Zhang G. Compact band weighting module based on attention-driven for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9540-9552

31. Qin Y, Tian P, Zhao L, Mutisya S, Jin J, Lu J, Hu X. Robustness of inverse solutions for radiative transfer parameters from light signals measured with different detection configurations. Journal of Quantitative Spectroscopy and Radiative Transfer, 2021, 274: 107883

32. Tian P, Qin Y, Zhao L, Mutisya S, Jin J, Lu J, Hu X. Multiparameter spectrophotometry platform for turbid sample measurement by robust solutions of radiative transfer problems. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-10

33. Wang W, Wen Y, Lu J, Zhao L, Safaa A, Hu X. Rapid classification of micron-sized particles of sphere, cylinders and ellipsoids by diffraction image parameters combined with scattered light intensity. Journal of Quantitative Spectroscopy and Radiative Transfer, 2019, 224: 453-459

34. Zhao L, Peng T, Xie Y, Yang C, Gui W, Zhao Y. Froth stereo visual feature extraction for the industrial flotation Process. Industrial & Engineering Chemistry Research, 2019, 58(31): 14510-14519

35. Tu B, Kuang W, Shang Y, He D, Zhao L. A multi-view object tracking using triplet model. Journal of Visual Communication and Image Representation, 2019, 60: 64-68

36. Zhao L, Peng T, Xie Y, Yang C, Gui W. Recognition of flooding and sinking conditions in flotation process using soft measurement of froth surface level and QTA. Chemometrics and Intelligent Laboratory Systems, 2017, 169: 45-52

37. Zhao L, Peng T, Zhao Y, Peng X, Xie Y. Features Extraction of flotation froth based on equivalent binocular stereo vision. IFAC-PapersOnLine, 2016, 49(20): 90-95

38. Zhao L, Peng T, Zhao L, Peng X, Zhao Y, Song Y. Fault condition recognition based on multi-scale texture features and embedding prior knowledge k-means for antimony flotation process. IFAC-PapersOnLine, 2015, 48(21): 864-870 (EI)

39. Peng X, Peng T, Zhao L, Song Y, Gui W. Working condition recognition based on an improved NGLDM and interval data-based classifier for the antimony roughing process. Minerals Engineering, 2016, 86: 1-9 (中科院SCI二区)

     

40. 部分毕业学生情况：

1. 课题组招生：

2. 我们希望入学后的你

   （1）扎实的算法功底

   学会如何找到学术前沿，如何发现科学问题并寻找解决方案，如何设计算法并编程实现，如何开展算法评估测试与迭代改进；

   （2）良好的表达能力

   学会如何写一篇逻辑严密、论证充分、表达清晰的学术论文；学会如何做一个优雅得体、引人入胜的学术报告；

   （3）开放的个人胸怀

   学会与团队的良好协作，与全国智力精英的思维碰撞与交流，与企业和学术界的交流与合作；掌握持续自我学习的能力。

3. 我们期待毕业后的你

   （1）进入国内一流企业从事前沿技术研发；

   （2）在国内一流高校甚至海外知名高校读博深造；

   （3）开展高科技领域创新创业。

    

4. 联系方式：

   地址：湖南理工学院19栋305室

   邮编：414006

   网址：https://ipcv.hnist.cn/info/1180/1115.htm

   部分论文源代码：https://github.com/zhaolin6

   Email：zhaol@hnist.edu.cn