一. 基本情况

杨彬、男、博士、副教授、博士生导师、湖南省优秀青年基金获得者。主要从事图像处理、模式识别、人工智能及其在遥感数据分析、空间信息智能处理等方面的应用研究。中国图象图形学学会遥感图像专业委会委员、中国遥感应用协会人工智能解译专业委员会委员、全国研究生教育评估监测专家库专家、湖南省现代农业产业技术体系智能与信息化岗位专家。近年来先后主持国家自然科学基金青年基金1项、国家重点研发项目任务1项、广西重点研发项目课题1项、湖南省自然科学基金青年基金1项、长沙市自然科学基金1项、中央高校基本科研业务费1项，参与国家自然科学基金重大项目1项，科技部重点研发专项1项等。担任RSE, TIP, TGRS, TIM, JSTARS, GRSL等期刊审稿人。

E-mail：binyang★hnu.edu.cn (请替换特殊字符★为@）

欢迎有志于从事图像处理、模式识别、人工智能等方面研究的同学报读我的研究生。

二. 教育和工作经历

2017/07—至今，       湖南大学电气与信息工程学院，助理教授、副教授

2012/09—2017/06， 北京大学摄影测量与遥感专业，博士

2014/09—2016/03， 美国波士顿大学地球与环境专业，联合培养

2008/09—2012/06， 北京航空航天大学遥感科学与技术专业，学士

三. 主讲课程

1. 信号与系统（获评首批国家级一流本科课程）

慕课地址：http://www.icourse163.org/course/HNU-1003363020

2. C++面向对象程序设计

3. 智能信息处理

四. 主要科研项目

l湖南省重点研发项目，蓝藻水华监测预警与应急处置体系研究，2023.07-2025.07，主持

l湖南省优秀青年基金，耦合光学偏振遥感的抗云雾干扰与成像时间差异地物变化检测，2023.01-2025.12，主持

l长沙市自然科学基金，面向小样本数据的高光谱遥感图像变化检测研究，2023.01-2024.12，主持

l国家重点研发项目，赣江流域农田重金属含量遥感智能反演，2022.11-2026.12，任务负责人

l湖南省农业农村厅科技创新项目，棉花虫害识别与产量估测智能化技术研究，2022.07-2023.06，主持

l国家卫星海洋应用中心项目，基于多源多时相遥感影像的洞庭湖区域变化检测分析，2020.08-2021.12，主持

l国家自然科学基金青年基金，基于偏振反射与光谱不变量的植被氮含量遥感反演，2019.01-2021.12，主持

l广西重点研发项目，广西自然保护区生态环境遥感监测与技术示范，2019.03-2021.02，课题负责人

l湖南省自科青年，基于随机辐射传输理论的农作物叶面积指数和叶绿素含量遥感反演，2019.01-2021.12，主持

l中央高校基本科研业务费，2017.07-2022.07，主持

五. 科研成果

lYang, B., Mao, Y., Liu, L., Liu, X., Ma, Y., Li, J. (2023). From Trained to Untrained: A Novel Change Detection Framework Using Randomly Initialized Models with Spatial-Channel Augmentation for Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing. doi: 10.1109/TGRS.2023.3262928. code.

lYe, X., Hui, J., Wang, P., Zhu J., Yang, B. (*) (2023). A Modified Transfer-learning-based Approach for Retrieving Land Surface Temperature from Landsat-8 TIRS Data. IEEE Transactions on Geoscience and Remote Sensing. doi: 10.1109/TGRS.2023.3333689.

lYang, B., Wang, Z., Guo, J., et al. (2023). Identifying plant disease and severity from leaves: A deep multitask learning framework using triple-branch Swin Transformer and deep supervision. Computers and Electronics in Agriculture. 209, 107809.

lYang, B., Guo, J., Liu, J., & Ye, X. (2022). PPCE: A Practical Loss for Crop Mapping Using Phenological Prior. IEEE Geoscience and Remote Sensing Letters. 20,1-5.

lLin, Y., Liu, S., Yan, L., Yan, K., Zeng, Y., Yang, B. (*) (2022). Improving the Estimation of Canopy Structure Using Spectral Invariants: Theoretical Basis and Validation. Remote Sensing of Environment, 284, 113368.

lYang, B., Qin, L., Liu, J., & Liu X. (2022). UTRNet: An Unsupervised Time-Distance-Guided Convolutional Recurrent Network for Change Detection in Irregularly Collected Images. IEEE Transactions on Geoscience and Remote Sensing. 60, 1-16. ESI高被引论文, code.

lLi, J., Zhu, J., Li, C., Chen, X., Yang, B. (*) (2022). CGTF: Convolution-Guided Transformer for Infrared and Visible Image Fusion. IEEE Transactions on Instrumentation and Measurement. 71, 1-14.

lYang, B., Qin, L., Liu, J., & Liu X. (2022).  IRCNN: An Irregular-Time-Distanced Recurrent Convolutional Neural Network for Change Detection in Satellite Time Series. IEEE Geoscience and Remote Sensing Letters. 19,1-5. code.

lZhang, F., Zhang, X., Chen, W. (*), Yang, B. (*),  Chen, Z., et al. (2022).  Cloud-free Land Surface Temperature Reconstructions Based on MODIS Measurements and Numerical Simulations for Characterizing Surface Urban Heat Islands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi: 10.1109/JSTARS.2022.3199248.

lLiu, S., Lin, Y., Yan, L. & Yang, B. (*) (2020). Modeling Bidirectional Polarization Distribution Function of Land Surfaces Using Machine Learning Techniques. Remote Sensing, 12(23), 3891.

lYang, B., Lin, H., & He, Y. (2020). Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations. Sensors, 20(18), 5394.

lHe, Y., Yang, B.(*), Lin, H., & Zhang, J. (2020). Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks. Remote Sensing, 12(2), 248.

lYang, B., He, Y., & Chen, W. (2020). A simple method for estimation of leaf dry matter content in fresh leaves using leaf scattering albedo. Global Ecology and Conservation, 23, e01201.

lYang, B., Zhao, H., & Chen, W. (2019). Modeling polarized reflectance of snow and ice surface using POLDER measurements. Journal of Quantitative Spectroscopy and Radiative Transfer, 236, 106578.

lYang, B., Knyazikhin, Y., Zhao, H., & Ma, Y. (2018). Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model. Agricultural and Forest Meteorology, 263, 477-482.

lYang, B., Knyazikhin, Y., Xie, D., Zhao, H., Zhang, J., & Wu, Y. (2018). Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model. Remote Sensing, 10, 1632.

lYang, B., Knyazikhin, Y., Mõttus, M., Rautiainen, M., Stenberg, P., Yan, L., Chen, C., Yan, K., Choi, S., Park, T., & Myneni, R.B. (2017). Estimation of leaf area index and its sunlit portion from DSCOVR EPIC data: Theoretical basis. Remote Sensing of Environment, 198, 69-84.

lYang, B., Zhao, H., & Chen, W. (2017). Semi-empirical models for polarized reflectance of land surfaces: Intercomparison using space-borne POLDER measurements. Journal of Quantitative Spectroscopy and Radiative Transfer, 202, 13-20.

lYang, B., Knyazikhin, Y., Lin, Y., Yan, K., Chen, C., Park, T., Choi, S., Mõttus, M., Rautiainen, M., Myneni, R., & Yan, L. (2016). Analyses of Impact of Needle Surface Properties on Estimation of Needle Absorption Spectrum: Case Study with Coniferous Needle and Shoot Samples. Remote Sensing, 8, 563.

六. 教材和专著

1. 《信号与系统分析》，副主编，华中科技大学出版社，2020

2. 《Polarization Remote Sensing Physics》，第二作者，Springer，2020