太阳集团tcy8722(中国)科技公司-BinG百科

杨斌（副教授）

发布时间： 2024-02-23

浏览量： 11282

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，获得电路与系统理学博士学位；期间曾被评为复旦大学优秀学生和上海市优秀毕业生。自2019年开始就职于太阳集团tcy8722，从事遥感图像处理、机器学习与人工智能、计算机软件设计和应用等方面的科研与教学工作。

IEEE Member，IEEE地球科学与遥感协会GRSS会员。在国内外学术期刊和会议中发表论文40余篇，多次以第一或通讯作者在遥感领域的国际权威期刊IEEE Transactions on Geoscience and Remote Sensing(TGRS)、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(JSTARS)和Remote Sensing上发表长篇研究成果。长期参与IEEE TGRS、IEEE JSTARS、IEEE Transactions on Image Processing(TIP)、IEEE Transactions on Aerospace and Electronic Systems(TAES)、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。

研究方向：（欢迎访问我们的实验室网站！）

高光谱图像处理的理论与方法

模式识别与机器学习

计算智能与人工智能方法（群智能、多目标进化优化、深度神经网络等）

**欢迎热爱科学研究、擅于思考和专研，虚心求学的相关专业（计算机、软件工程、数学、遥感、地理信息系统和电子信息科学等）同学报考；要求具有良好的数学、英语基础和计算机编程能力，优先考虑具有数学建模或科研经历者；每年拟招收1-2名专硕、1-2名学硕。

讲授课程：

本科：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）

主要研究成果：

近期的代表性期刊论文

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 Transactions on Geoscience and Remote Sensing, 2024.(中科院SCI一区，IF: 8.2，通讯作者，Early Access)

2. Huangying Zhang(所指导硕士生), Bin Yang*. Geometrical projection improved multi-objective particle swarm optimization for unsupervised nonlinear hyperspectral unmixing. International Journal of Remote Sensing, 2024. (中科院SCI三区，IF: 3.4，通讯作者，已录用)

3. Zhangqiang Yin(所指导硕士生), Bin Yang*.. Unsupervised nonlinear hyperspectral unmixing with reduced spectral variability via superpixel-based fisher transformation. Remote Sensing, 15(20): 1-25, 2023. (中科院SCI二区，IF: 5.0，通讯作者)

4. 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 Transactions on Geoscience and Remote Sensing, 61: 1-15, 2023. (中科院SCI一区，IF: 8.2，通讯作者)

5. Danni Jin (所指导硕士生) and Bin Yang*. Graph attention convolutional autoencoder-based unsupervised nonlinear unmixing for hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 7896-7906, 2023. (中科院SCI二区(top)，IF: 5.5，通讯作者)

6. Minglei Li, Bin Yang*, and Bin Wang.Spectral-spatial reweighted robust nonlinear unmixing for hyperspectral images based on an extended multilinear mixing model.IEEE Transactions on Geoscience and Remote Sensing, 60, 2022. (中科院SCI一区，IF: 8.2，通讯作者)

7. Bin Yang*. Supervised nonlinear hyperspectral unmixing with automatic shadow compensation using multi-swarm particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing, 60: 1–18, 2022. (中科院SCI一区，IF: 8.2)

8. Yapeng Miao (所指导硕士生) and Bin Yang*. Multi-level reweighted sparse hyperspectral unmixing using superpixel segmentation and particle swarm optimization. IEEE Geoscience and Remote Sensing Letters, 19:1-5, 2022. (中科院SCI二区，IF: 4.8，通讯作者)

9. Jiafeng Gu,Bin Yang, and Bin Wang. Nonlinear unmixing for hyperspectral images via kernel-transformed bilinear mixing models. IEEE Transactions on Geoscience and Remote Sensing, 60: 1–13, 2022. (中科院SCI一区，IF: 8.2)

10. Muhammad Sohail, Zhao Chen, Bin Yang, Guohua Liu. Multiscale spectral-spatial feature learning for hyperspectral image classification. Displays, 74: 102278, 2022. (中科院SCI二区，IF: 4.3)

11. Zexing Zhang (所指导硕士生) and Bin Yang*. Hypergraph regularized deep autoencoder for unsupervised unmixing hyperspectral images. Journal of Donghua University (English Edition), 40(1): 8-17, 2023. (通讯作者)

12.杨斌*, 王斌. 光谱解混技术及其应用研究进展. 激光与光电子学进展, 2021, 58(16): 1600004, 1–38. (ESCI、中文核心，当选优秀论文)

13. Yapeng Miao (所指导硕士生) and Bin Yang*. Sparse unmixing for hyperspectral imagery via comprehensive-learning-based particle swarm optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14(1): 9727–9742. (中科院SCI二区(top)，IF: 5.5，通讯作者)

14. Bin Yang, Zhao Chen, and Bin Wang. Nonlinear endmember identification for hyperspectral imagery via hyperpath-based simplex growing and fuzzy assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13(1): 351–366. (中科院SCI二区(top)，IF: 5.5)

15. Bin Yang and Bin Wang. Band-wise nonlinear unmixing for hyperspectral imagery using an extended multilinear mixing model. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6747–6762. (中科院SCI一区，IF: 8.2)

16. Bin Yang, Bin Wang, and Zongmin Wu. Nonlinear hyperspectral unmixing based on geometric characteristics of bilinear mixture models. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 694–714. (中科院SCI一区，IF: 8.2)

17. 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 Sensing, 2018, 10(5): 801, 1–30. (中科院SCI二区，IF: 5.0)

18. Bin Yang, Wenfei Luo, and Bin Wang. Constrained nonnegative matrix factorization based on particle swarm optimization for hyperspectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3693–3710. (中科院SCI二区(top)，IF: 5.5)

19. 杨斌, 王斌, 吴宗敏. 基于双线性模型的高光谱图像非线性解混, 红外与毫米波学报, 2018, 37(5): 631–641. （中科院SCI四区，IF：0.266）

20. Zehao Chen, Bin Yang, Bin Wang. A preprocessing method for hyperspectral target detection based on tensor principal component analysis. Remote Sensing, 2018, 10(7): 1033, 1–21. （中科院SCI二区，IF: 5.0）

21. 杨斌, 王斌. 高光谱遥感图像非线性解混研究综述, 红外与毫米波学报, 2017, 36(2): 173–185. （中科院SCI四区，IF：0.266）

22. 杨斌, 罗文斐. 约束非负矩阵分解框架下高维自适应粒子群端元提取, 遥感学报, 2015, 19(02): 240–253. (EI检索)

23. Wenfei Luo, Lianru Gao, Antonio Plaza, Andrea Marinoni, Bin Yang, Liang Zhong, Paolo Gamba, Bing Zhang. A new algorithm for bilinear spectral unmixing of hyperspectral images using particle swarm optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(12): 5776–5790. (中科院SCI二区(top)，IF：5.5)

近期主持的科研项目：

1.国家自然科学基金青年项目，“基于群智能多目标优化的高光谱遥感图像非线性解混方法研究”，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，“河口海岸复杂水体遥感图像的非线性光谱解混方法研究”，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，“基于空谱流形信息的高光谱遥感图像非线性解混方法研究”，2020.01 – 2022.12 （编号：2232020D-33，结题，主持）

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, 专利申请号： 201611062937.7

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，专利申请号： 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

联系方式：

办公室：上海市松江区人民北路2999号太阳集团tcy87221号学院楼108

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

Lecturer, Master Supervisor, Member of Jiusan Society

Bio:

He graduated from the School of Information Science and Technology of Fudan University in 2019 and received the Ph.D. degree in Circuits and Systems. During this period, he was awarded as Outstanding Student of Fudan University and Outstanding Graduate of Shanghai. Since July 2019, he has been working in the School of Computer Science and Technology of Donghua University, engaged in scientific research and education in remote sensing image processing, machine learning and artificial intelligence, computer software design and applications, etc.

He is an IEEE Member and an IEEE Geoscience and Remote Sensing Society Member. Over the past 5 years, as the first or corresponding author, he has published more than 30 papers in domestic and international 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) and Remote Sensing. He has been a reviewer for TGRS, JSTARS, IEEE Transactions on Image Processing (TIP), IEEE Transactions on Aerospace and Electronic Systems (TAES), Neural Networks, and other academic journals for a long time.

Research Areas: (Welcome to visit our Lab Website!)

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)

**Students with a passion for scientific research and an open mind to study are welcome. Good knowledge of mathematics and English (CET-6, TOFEL, etc.) and computer programming skills are required. Experience in mathematical modeling or scientific research is preferred. We plan to recruit 1-2 professional masters and 1-2 academic master students every year.

Main Courses Taught:

Undergraduate Courses：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 Transactions on Geoscience and Remote Sensing, 2024.(SCI, JCR: Q1, IF: 8.2, Corresponding Author, Early Access)

2. Huangying Zhang, Bin Yang*. Geometrical projection improved multi-objective particle swarm optimization for unsupervised nonlinear hyperspectral unmixing. International Journal of Remote Sensing, 2024. (Accepted, SCI, JCR: Q2, IF: 3.4, Corresponding Author)

3. Zhangqiang Yin, Bin Yang*.. Unsupervised nonlinear hyperspectral unmixing with reduced spectral variability via superpixel-based fisher transformation. Remote Sensing, 15(20): 1-25, 2023. (SCI, JCR: Q1, IF: 5.0, Corresponding Author)

5. Danni Jinand Bin Yang*. Graph attention convolutional autoencoder-based unsupervised nonlinear unmixing for hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 7896-7906, 2023. (SCI, JCR: Q2, IF: 5.5, Corresponding Author)

8. Yapeng Miao and Bin Yang*. Multi-level reweighted sparse hyperspectral unmixing using superpixel segmentation and particle swarm optimization. IEEE Geoscience and Remote Sensing Letters, 2022. (SCI, JCR: Q1，IF: 4.8，Corresponding Author)

11. Zexing Zhang and Bin Yang*. Hypergraph regularized deep autoencoder for unsupervised unmixing hyperspectral images. Journal of Donghua University (English Edition), 39(6): 1-13, 2022. (Corresponding Author)

12.Bin Yang*, Bin Wang. Research advances of spectral unmixing technology and its applications. Laser & Optoelectronics Progress, 2021, 58(16): 1600004, 1–38. (ESCI、CSCD，outstanding paper)

13. Yapeng Miao and Bin Yang*. Sparse unmixing for hyperspectral imagery via comprehensive-learning-based particle swarm optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14(1): 9727–9742. (SCI, JCR: Q2，IF: 5.5，Corresponding Author)

19. Bin Yang, Bin Wang, Zongmin Wu. Nonlinear spectral unmixing for hyperspectral imagery based on bilinear mixture models, Journal of Infrared and Millimeter Waves, 2018, 37(5): 631–641. (SCI, JCR: Q4，IF：0.266)

21. Bin Yang, Bin Wang. Review of nonlinear unmixing for hyperspectral remote sensing imagery, Journal of Infrared and Millimeter Waves, 2017, 36(2): 173–185. (SCI, JCR: Q4，IF：0.266)

22. Bin Yang, Wenfei Luo. Constrained NMF-based high-dimension adaptive particle swarm optimization algorithm for endmember extraction from a hyperspectral remote sensing image, Journal of Remote Sensing, 2015, 19(02): 240–253. (EI)

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, “Research on Nonlinear Unmixing Methods for Hyperspectral Remote Sensing Imagery based on Swarm Intelligent Multi-objective Optimization”，2021.01-2023.12 (No. 62001098)

2.Natural Science Foundation of Shanghai, “Research on Nonlinear Unmixing Methods for Remote sensing images of complex estuarine and coastal water bodies”，2023.04–2026.03 (No：23ZR1402400)

3.Fundamental Research Funds for the Central Universities, “Research on Spatial-spectral Manifold Information based Nonlinear Unmixing Methods for Hyperspectral Remote Sensing Images”，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