讲座题目：非线性高光谱解混的新进展 New Developments in Nonlinear Hyperspectral Unmixing

报 告 人：Paul Scheunders

时 　 间：2019年10月25日(周五)10:00-12:00

地 　 点：中关村校区信息实验楼202报告厅

主办单位：研究生院、信息与电子学院

报名方式：登录北京理工大学微信企业号---第二课堂---课程报名中选择“【百家大讲堂】第252期：非线性高光谱解混的新进展”

【主讲人简介】

  1990年，Paul Scheunders在比利时安特卫普大学获得了统计力学领域的物理学博士学位。1992年，他成为安特卫普大学物理系视觉实验室的一名助理研究员，目前是该实验室的一名教授。他目前的研究兴趣是遥感图像处理，尤其是高光谱图像处理。他在图像处理、模式识别和遥感领域的国际期刊和会议记录上发表了250多篇论文。
  Paul Scheunders是IEEE Transactions on Geoscience and Remote Sensing 和Remote Sensing (MDPI)的副主编，并在许多国际会议上担任项目委员会成员，还是IEEE Geoscience and Remote Sensing Society的高级会员。

Paul Scheunders received the Ph.D. degree in physics, with work in the field of statistical mechanics, from the University of Antwerp, Antwerp, Belgium, in 1990. In 1992, he became a research associate with the Vision Lab, Department of Physics, University of Antwerp, where he is currently a professor. His current research interest includes remote sensing and in particular hyperspectral image processing. He has published over 250 papers in international journals and conference proceedings in the field of image processing, pattern recognition and remote sensing.

Paul Scheunders is Associate Editor of the IEEE Transactions on Geoscience and Remote Sensing and of Remote Sensing (MDPI) and has served as program committee member in numerous international conferences. He is senior member of the IEEE Geoscience and Remote Sensing Society.

【讲座信息】

  光谱解混是以信号的纯组分（端元光谱）和端元丰度的函数描述高光谱信号的过程。由于植被的多重反射或精细混合以及混合和复合材料化学性质的变化，光谱反射率呈现高度非线性。本次报告首先通过光谱解混过程的图示来讨论一种基于模型的非线性光谱解混方法。将重点讨论一种多线性解混模型以及包含阴影效应的拓展。其次，报告将讨论一种新的采用数据驱动的非线性光谱解混，其需要真值的端元光谱和丰度。该方法适用于很多近距离应用，如岩芯样品、混合和复合材料的表征以及叶片参数估计。

Spectral unmixing is the process of describing a hyperspectral signal in function of its pure constituents (endmember spectra) and their fractional abundances. The spectral reflectance can show highly nonlinear behavior, because of multiple reflections in vegetation or intimate mixing and changes in chemical properties of mixed and compound materials. This talk will first discuss a model-based approach for nonlinear spectral unmixing based on a graph description of the spectral mixing process. In particular, a multilinear mixing model and an extension including shadow effects will be discussed. Secondly, the talk will discuss a new data-driven approach to nonlinear spectral unmixing, which requires ground truth endmember spectra and fractional abundances. This approach will be applied to a number of close range applications such as the characterization of drill core samples, mixed and compound materials, as well as leaf parameter estimation.