杨斌（副教授）

发布时间： 2024-11-11

浏览量： 12123

杨斌

男副教授计算机软件与理论系

硕士生导师九三学社社员

个人简介

毕业于复旦大学信息科学与工程学院，获得理学博士学位；期间曾被评为复旦大学优秀学生和上海市优秀毕业生。目前在东华大学计算机科学与技术学院，从事图像智能解译、机器学习与人工智能等方面的科研与教学工作。

主持国家自然科学基金和上海市自然科学基金等科研项目5项，在国内外学术期刊和会议中发表论文40余篇，参与编著出版中英文学术专著2部。代表性的研究成果主要收录于IEEE Transactions on Geoscience and Remote Sensing(TGRS)、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(JSTARS)等JCR 1/JCR 2区国际权威期刊。长期参与IEEE TGRS、IEEE JSTARS、Neural Networks等学术期刊审稿工作。教育部学位中心本科和硕士学位论文评审专家，IEEE Member，中国图象图形学学会会员，中国电子学会会员，中国计算机学会会员。

研究方向

高光谱图像智能解译

机器学习与人工智能（群智能、多目标优化、深度神经网络等）

盲源分离反问题的建模与求解

讲授课程

离散数学，人工智能，.NET技术，机器学习及应用（属新材料现代产业学院）

研究生培养

↓↓起点不是终点，勇攀优秀之巅。科研没有捷径，唯有坚持不懈。↓↓

*已毕业硕士生5人，在读5人；研究生已发表SCI期刊论文7篇，获软件著作权1项

*已指导2名研究生获国家奖学金，其中1人同时获得上海市优秀毕业生、2023年度上海市计算机学会优秀硕士学位论文提名奖。

*本科生比赛与获奖：指导本科生获批2项国家级大学生创新创业项目，第二十五届中国机器人及人工智能大赛上海赛区比赛人工智能创新赛三等奖

*特别支持学生以“第一作者”发表高质量研究成果

##基本要求##：具有良好的数学、英语基础和编程能力，有科研或数学建模等竞赛经历者将被重点考虑

##核心要求##：对科研有浓厚的兴趣、具有良好的自主学习能力，自我要求高并持之以恒努力，与导师及课题组保持积极正向沟通的学生

##培养特色##：塑造科学研究的辩证思维，既有理学思维又有工学技术和动手能力，能够独立探索和解决计算机视觉与图像智能解译中的复杂问题，并持续地创新

主持的科研项目：

1.国家自然科学基金青年项目，2021. 01–2023.12 （编号：62001098，结题，主持）

2. 上海市自然科学基金面上项目，2023.04–2026.03（编号：23ZR1402400，在研，主持）

3.中央高校基本科研业务费专项资金自由探索项目，2020.01 – 2022.12 （编号：2232020D-33，结题，主持）

4. 复旦大学电磁波信息科学教育部重点实验室开放基金，2024.07–2025.06（编号：EMW202402，在研，主持）

5. 上海泰坦科技股份有限公司，化合物产品数据的智能分析与查询系统开发，2020.07–2021.04（企业技术开发横向课题，结题，主持）

主要研究成果：

代表性期刊论文

1. Minglei Li, Bin Yang*, and Bin Wang, “EMLM-Net: An extended multilinear mixing model-inspired dual-stream network for unsupervised nonlinear hyperspectral unmixing,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–16, 2024. (中科院SCI一区，IF: 7.5，DOI: 10.1109/TGRS.2024.3363427, *通讯作者)

2. Minglei Li, Bin Yang*, and Bin Wang, “A coarse-to-fine scheme for unsupervised nonlinear hyperspectral unmixing based on an extended multilinear mixing model,”IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–15, 2023. (中科院SCI一区，IF: 7.5，DOI: 10.1109/TGRS.2023.3308211, *通讯作者)

3. Bin Yang*, “Supervised nonlinear hyperspectral unmixing with automatic shadow compensation using multiswarm particle swarm optimization,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–18, 2022. (中科院SCI一区，IF: 7.5，DOI: 10.1109/TGRS.2022.3177648)

4. Minglei Li, Bin Yang*, and Bin Wang, “Spectral–spatial reweighted robust nonlinear unmixing for hyperspectral images based on an extended multilinear mixing model,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–17, Jul. 2022. (中科院SCI一区，IF: 7.5，DOI: 10.1109/TGRS.2022.3223434, *通讯作者)

5. Jiafeng Gu, Bin Yang, and Bin Wang, “Nonlinear unmixing for hyperspectral images via kernel-transformed bilinear mixing models,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–13, 2022. (中科院SCI一区，IF: 7.5，DOI: 10.1109/TGRS.2021.3135571)

6. Bin Yang and Bin Wang, “Band-wise nonlinear unmixing for hyperspectral imagery using an extended multilinear mixing model,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 11, pp. 6747–6762, 2018. (中科院SCI一区，IF: 7.5，DOI: 10.1109/TGRS.2018.2842707)

7. Bin Yang, Bin Wang, and Zongmin Wu, “Nonlinear hyperspectral unmixing based on geometric characteristics of bilinear mixture models,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 2, pp. 694–714, Feb. 2018. (中科院SCI一区，IF: 7.5，DOI: 10.1109/TGRS.2017.2753847)

8. Bin Yang, Zhao Chen, and Bin Wang, “Nonlinear endmember identification for hyperspectral imagery via hyperpath-based simplex growing and fuzzy assessment,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 351–366, 2020. (中科院SCI二区(top)，IF: 4.7，DOI: 10.1109/JSTARS.2019.2962609)

9. Bin Yang, Wenfei Luo, and Bin Wang, “Constrained nonnegative matrix factorization based on particle swarm optimization for hyperspectral unmixing,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 8, pp. 3693–3710, Aug. 2017. (中科院SCI二区(top)，IF: 4.7，DOI: 10.1109/JSTARS.2017.2682281)

10. Wenfei Luo, Lianru Gao, Antonio Plaza, Andrea Marinoni, Bin Yang, Liang Zhong, Paolo Gamba, and Bing Zhang, “A new algorithm for bilinear spectral unmixing of hyperspectral images using particle swarm optimization,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 12, pp. 5776–5790, Dec. 2016. (中科院SCI二区(top)，IF: 4.7，DOI: 10.1109/JSTARS.2016. 2602882)

11. Bin Yang, Bin Wang, and Zongmin Wu, “Unsupervised nonlinear hyperspectral unmixing based on bilinear mixture models via geometric projection and constrained nonnegative matrix factorization,” Remote Sens., vol. 10, no. 5, pp. 801(1–30), May. 2018. (中科院SCI二区，IF: 4.2，DOI: 10.3390/rs10050801)

12. Zehao Chen,Bin Yang, and Bin Wang, “A preprocessing method for hyperspectral target detection based on tensor principal component analysis,” Remote Sens., vol. 10, no. 7, pp. 1033(1–21), Jun. 2018. (中科院SCI二区，IF: 4.2，DOI: 10.3390/rs10071033)

13. Muhammad Sohail, Zhao Chen, Bin Yang, Guohua Liu, “Multiscale spectral-spatial feature learning for hyperspectral image classification,” Displays, vol. 74, no. June, p. 102278, 2022. (DOI: 10.1016/j.displa.2022.102278)

14.Bin Yang* and Zhangqiang Yin, Spectral variability augmented multi-linear mixing model for hyperspectral nonlinear unmixing. IEEE Geosci. Remote Sens. Lett., 2024. (中科院SCI三区，IF: 4.0，DOI: 10.1109/LGRS.2024.3482103)

研究生一作期刊论文

15. Zhenyu Ma and Bin Yang*, Spatial-spectral hypergraph-based unsupervised band selection for hyperspectral remote sensing images.IEEE Sens. J., vol. 24, no. 17, pp. 27870-27882, September. 2024. (中科院SCI二区，IF: 4.3，DOI: 10.1109/JSEN.2024.3431241, *通讯作者)

16. Huangying Zhang and Bin Yang*, “Geometrical projection improved multi-objective particle swarm optimization for unsupervised nonlinear hyperspectral unmixing,” Int. J. Remote Sens., vol. 45, no. 6, pp. 1849–1882, Mar. 2024. (中科院SCI三区，IF: 3.4，DOI: 10.1080/01431161.2024.2320181, *通讯作者)

17. Zhangqiang Yin and Bin Yang*, “Unsupervised nonlinear hyperspectral unmixing with reduced spectral variability via superpixel-based fisher transformation,” Remote Sens., vol. 15, no. 20, p. 5028, Oct. 2023. (中科院SCI二区，IF: 4.2，DOI: 10.3390/rs15205028, *通讯作者)

18. Yapeng Miao and Bin Yang*, “Multilevel reweighted sparse hyperspectral unmixing using superpixel segmentation and particle swarm optimization,” IEEE Geosci. Remote Sens. Lett., vol. 19, 2022. (中科院SCI三区，IF: 4.0，DOI: 10.1109/LGRS.2022.3203990, *通讯作者)

19. Yapeng Miao and Bin Yang*, “Sparse unmixing for hyperspectral imagery via comprehensive-learning-based particle swarm optimization,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 9727–9742, 2021. (中科院SCI二区(top)，IF: 4.7，DOI: 10.1109/JSTARS.2021.3115177, *通讯作者)

20. Danni Jin and Bin Yang*, “Graph attention convolutional autoencoder-based unsupervised nonlinear unmixing for hyperspectral images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, pp. 7896–7906, 2023. (中科院SCI二区(top)，IF: 4.7，DOI: 10.1109/JSTARS.2023.3308037, *通讯作者)

21. Zexing Zhang and Bin Yang*, “Hypergraph regularized deep autoencoder for unsupervised unmixing hyperspectral images,” J. Donghua Univ. (English Ed.), vol. 40, no. 1, pp. 8–17, 2023. (DOI: 10.19884/j.1672-5220.202201002, *通讯作者)

近几年的论著、专利：

专著

1.王斌, 杨斌. 高光谱遥感图像解混理论与方法——从线性到非线性. 北京: 科学出版社, 2019.

国家发明专利

1.杨斌, 王斌. 一种基于双线性模型的高光谱图像非线性解混方法, 专利号： CN201611062937.7

2.陈昭，杨斌，郑雨欣. 基于流形回归网络的细胞定位与计数方法及应用，专利号： 202111059720.1

3. 徐春晓，刘国华，杨斌等. 一种用于纺织服装工业互联网中的订单分配方法，专利号：ZL 202110807678.0

软件著作权

1. 尹张强，杨斌. 高光谱图像群智能解混系统V1.0，登记号：2024SR0846342

联系方式：

办公室：上海市松江区人民北路2999号东华大学1号学院楼108

研究生实验室：图文信息中心703

邮编：201620

办公电话：(86)021-67792382

电子邮箱：yangb19@dhu.edu.cn

Bin Yang

Associate Professor, Master Supervisor, Member of Jiusan Society

Bio:

He graduated from the School of Information Science and Technology of Fudan Universityand received the Ph.D. degree in 2019. During this period, he was awarded as Outstanding Student of Fudan University and Outstanding Graduate of Shanghai. He is an associate professor in the School of Computer Science and Technology of Donghua University, engaged in scientific research and education in image processing, machine learning and artificial intelligence, etc.

He has published more than 40 papers in academic journals and conferences including IEEE Transactions on Geoscience and Remote Sensing (TGRS), IEEE Journal of Selected Topics in Geoscience and Remote Sensing Applied Earth Observations and Remote Sensing (JSTARS). He has been a reviewer for TGRS, JSTARS, Neural Networks, and other academic journals for a long time.

Research Areas:

1. Hyperspectral Image Processing Theories and Methods

2. Pattern Recognition and Machine Learning

3. Computational Intelligence and Artificial Intelligence (e.g., Swarm Intelligence, Multi-Objective Evolutionary Optimization, Deep Neural Networks)

Main Courses Taught:

Discrete Mathematics, Artificial Intelligence, .NET Technology, Machine Learning and Application (Course for College of Modern Industry Advanced Materials)

Publications:

Journal Papers:

1. Minglei Li, Bin Yang*, and Bin Wang, “EMLM-Net: An extended multilinear mixing model-inspired dual-stream network for unsupervised nonlinear hyperspectral unmixing,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–16, 2024. (DOI: 10.1109/TGRS.2024.3363427, *Corresponding Author)

2. Minglei Li, Bin Yang*, and Bin Wang, “A coarse-to-fine scheme for unsupervised nonlinear hyperspectral unmixing based on an extended multilinear mixing model,”IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–15, 2023. (DOI: 10.1109/TGRS.2023.3308211, *Corresponding Author)

4. Minglei Li, Bin Yang*, and Bin Wang, “Spectral–spatial reweighted robust nonlinear unmixing for hyperspectral images based on an extended multilinear mixing model,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–17, Jul. 2022. (DOI: 10.1109/TGRS.2022.3223434, *Corresponding Author)

15. Zhenyu Ma and Bin Yang*, Spatial-spectral hypergraph-based unsupervised band selection for hyperspectral remote sensing images.IEEE Sens. J., vol. 24, no. 17, pp. 27870-27882, September. 2024. (DOI: 10.1109/JSEN.2024.3431241, *Corresponding Author)

16. Huangying Zhang, Bin Yang*, “Geometrical projection improved multi-objective particle swarm optimization for unsupervised nonlinear hyperspectral unmixing,” Int. J. Remote Sens., vol. 45, no. 6, pp. 1849–1882, Mar. 2024. (DOI: 10.1080/01431161.2024.2320181, *Corresponding Author)

17. Zhangqiang Yin, Bin Yang*, “Unsupervised nonlinear hyperspectral unmixing with reduced spectral variability via superpixel-based fisher transformation,” Remote Sens., vol. 15, no. 20, p. 5028, Oct. 2023. (中科院SCI二区，IF: 4.2，DOI: 10.3390/rs15205028, *Corresponding Author)

19. Yapeng Miao and Bin Yang*, “Sparse unmixing for hyperspectral imagery via comprehensive-learning-based particle swarm optimization,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 9727–9742, 2021. (DOI: 10.1109/JSTARS.2021.3115177, *Corresponding Author)

20. Danni Jin and Bin Yang*, “Graph attention convolutional autoencoder-based unsupervised nonlinear unmixing for hyperspectral images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, pp. 7896–7906, 2023. (DOI: 10.1109/JSTARS.2023.3308037, *Corresponding Author)

Academic Monograph:

1. Bin Wang, Bin Yang. Hyperspectral remote sensing image unmixing theories and methods: From linear to nonlinear. Beijing: Science Press, 2019.

Patents:

1.Bin Yang, Bin Wang. A bilinear model based nonlinear unmixing method for hyperspectral images, No. 201611062937.7

2. Zhao Chen, Bin Yang, and Yuxin Zhen. Manifold regression network-based cell location and counting method and its application, No. 202111059720.1

3. Chunxiao Xu, Guohua Liu, Bin Yang, et al. An order distribution method for textile and garment industrial Internet, No. ZL 202110807678.0

Main Research Projects:

1.National Natural Science Foundation of China, 2021.01-2023.12 (No. 62001098)

2.Natural Science Foundation of Shanghai, 2023.04–2026.03 (No：23ZR1402400)

3.Fundamental Research Funds for the Central Universities,2020.01-2022.12 (No. 2232020D-33)

Address:

Office: Room 108, Department Building 1 of Donghua University, No. 2999, Renmin North Road, Songjiang District, Shanghai

Lab: Room 703, Library and Information Center of Donghua University

Tel: (86)021-67792382

Mail: yangb19@dhu.edu.cn; Postcode: 201620