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Dr. Bin Yang

Dr. Bin Yang

Education

  • Ph.D. degree in Electronic Engineering from Fudan University, Shanghai, China, 2019.
  • Main research interest: Hyperspectral Remote Sensing Image Analysis, Machine Learning, Pattern Recognition, Computational Intelligence, and Multi-objective Optimization.

Teaching and Academic Service

  • Current Position: Lecturer
  • Courses: .NET Technique, Introduction to Artificial Intelligence, Discrete Mathematics
  • Academic Service: Reviewer for journals including IEEE Transactions on Geoscience and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, and IEEE Transactions on Image Processing.

Research Experiences

Research Interests

My current research work is mainly on the development of hyperspectral remote sensing image processing methods. I am devoted to develop spectral unmixing methods to deal with the intrinsic issue of mixed pixels in hyperspectral remote sensing imagery, which helps to obtain the pure materials’ spectrum (i.e., endmembers) and their corresponding fractional abundances at the sub-pixel level, and improve the accuracies of various remote sensing applications. Theories and techniques of pattern recognition, machine learning, signal processing and mathematical modeling in the fields of computer science and mathematics, and their applications in remote sensing are also my specific interests.

Project Experiences

  1. “Research on nonlinear unmixing theoretical methods and applications for hyperspectral remote sensing imagery” supported by National Natural Science Foundation of China under Grant 61572133.
  2. “Research on high-performance and parallel linear unmixing algorithms for hyperspectral images and applications in fast target detection” supported by National Natural Science Foundation of China under Grant 41171288.
  3. “Research on nonlinear unmixing for hyperspectral remote sensing imagery” supported by the Research Fund for the State Key Laboratory of Earth Surface Processes and Resource Ecology under Grant 2017-KF-19.
  4. “Constrained nonnegative matrix factorization based high-dimensional adaptive particle swarm optimization algorithm for spectral unmixing” supported by Graduate Research Innovation Fund of School of Geography of South China Normal University (Project Leader).

Conference Experiences

  1. Poster presentation in 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, July 22-27, 2018.
  2. Oral presentation in the 4th Chinese Imaging Spectrometry Technique and Application Symposium, Harbin, Heilongjiang, China, September 10-12, 2017.
  3. Oral and poster presentations in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Forth Worth, Texas, USA, July 23-28, 2017.

Publications (2015 to Present)

Journal Papers

  1. 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, Nov. 2018. (DOI: 10.1109/TGRS.2018.2842707)
  2. 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. (DOI: 10.1109/TGRS.2017.2753847)
  3. 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. (DOI: 10.3390/rs10050801)
  4. 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. (DOI: 10.1109/JSTARS.2017.2682281)
  5. Bin Yang, Bin Wang, and Zongmin Wu, “Nonlinear spectral unmixing for hyperspectral imagery based on bilinear mixture models,” Infrared Millim.Waves., vol. 37, no. 5, pp. 631–641, 2018.
  6. Bin Yang and Bin Wang, “Review of nonlinear unmixing for hyperspectral remote sensing imagery,” Infrared Millim.Waves., vol. 36, no. 2, pp. 173–185, Apr. 2017. (in Chinese)
  7. Bin Yang and Wenfei Luo, “Constrained NMF based high-dimension adaptive particle swarm optimization algorithm for endmember extraction from hyperspectral remote sensing image,” Journal of Remote Sensing, vol. 19, no. 2, pp. 240–253, 2015. (in Chinese)
  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. Topics Appl. Earth Observ. Remote Sens., vol. 13, no. 1, pp. 351–366, Jan. 2020.
  9. 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. (DOI: 10.3390/rs10071033)
  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. (DOI: 10.1109/JSTARS.2016. 2602882)
  11. Tongxiang Zhi, Bin Yang, and Bin Wang, “A nonlinear unmixing algorithm dealing with spectral variability for hyperspectral imagery,” Infrared Millim.Waves., 2018. (accepted, in Chinese)
  12. Tongxiang Zhi, Bin Yang, and Bin Wang, “Hyperspectral nonlinear unmixing based on abundances constrained kernel nonnegative matrix factorization,” Journal of Fudan University (Natural Science), 2018. (accepted, in Chinese)
  13. Shiyin Qin, Wenfei Luo, Bin Yang, and Ruihao Zhang, “Simplex volume minimization based differential evolution algorithm for spectral unmixing,” Journal of Image and Graphics, vol. 20, no. 11, pp. 1535–1544, 2015. (in Chinese)

Conference Papers

  1. Bin Yang, Bin Wang, Bo Hu, and Jian Qiu Zhang, “Nonlinear hyperspectral unmixing via modelling band dependent nonlinearity,” 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’18), Valencia, Spain, 2018, 2693–2696.
  2. Bin Yang, Bin Wang, Zongmin Wu, and Qiyong Lu, “Bilinear mixture models based unsupervised nonlinear unmixing using constrained nonnegative matrix factorization,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 23-28 July 2017, 582–585. (DOI: 10.1109/IGARSS.2017.8127020)
  3. Bin Yang, Bin Wang, Zongmin Wu, and Qiyong Lu, “Abundance estimation for hyperspectral images based on bilinear mixture models,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 23-28 July 2017, 644–647. (DOI: 10.1109/IGARSS.2017.8127036)
  4. Zhao Chen, Bin Yang, and Bin Wang, “Hyperspectral target detection: A preprocessing method based on tensor principal component analysis,” 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’18), Valencia, Spain, 22-27 July 2018. (Accepted)
  5. Tongxiang Zhi, Bing Yang, Zhao Chen, and Bin Wang, “Nonnegative matrix factorization with constraints on endmember and abundance for hyperspectral unmixing,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 2017, 1149–1152. (DOI: 10.1109/IGARSS.2017.8127161)
  6. Zhao Chen, Bin Yang, Bin Wang, Guohua Liu, and Wei Xia, “Change detection in hyperspectral imagery based on spectrally-spatially regularized low-rank matrix decomposition,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’17), Fort Worth, TX, 23-28 July 2017, 157–160. (DOI: 10.1109/IGARSS.2017.8126918)

Patent Application

Bin Yang and Bin Wang. A hyperspectral nonlinear unmixing method based on bilinear mixture models, Application No. 201611062937.7.

Academic Book

Bin Wang and Bin Yang, Theories and Methods of Spectral Unmixing for Hyperspectral Remote Sensing Imagery: From Linearity to Nonlinearity. Beijing, China: Science Press, 2019. (in Chinese)

Awards

  • Outstanding Graduate of Shanghai Higher Education Institutions
  • National Scholarship of Fudan University for doctoral candidates
  • Outstanding Student of Fudan University