英国赫瑞瓦特大学（Heriot-Watt University）Stephen McLaughlin教授学术讲座预告
报告题目：Fast Hyperspectral Unmixing in Presence of Nonlinearity or Mismodelling Effects.
报告人：Stephen McLaughlin教授
时间：2016年11月25日（周五）上午9:00
地点：中科院遥感地球所（奥运园区）A503会议室
报告人简介：
Stephen McLaughlin教授，现为皇家工程学会、爱丁堡皇家学会、工程与技术学会（IET），电子与电气工程师学会（IEEE）会员，赫瑞瓦特大学（Heriot-Watt University）工程与物理科学学院院长，研究领域包括自适应信号处理算法、非线性动力系统理论等方面，其研究成果应用在生物医学工程、能源与通信系统等方面，是信号处理方面的专家。
报告简介：
This talk will discuss two novel hyperspectral mixture models and associated unmixing algorithms. The two models assume a linear mixing model corrupted by an additive term whose expression can be adapted to account for multiple scattering nonlinearities (NL), or mismodelling effects (ME). The NL model generalizes bilinear models by taking into account higher order interaction terms. The ME model accounts for different effects such as endmember variability or the presence of outliers. The abundance and residual parameters of these models are estimated by considering a convex formulation suitable for fast estimation algorithms. This formulation accounts for constraints such as the sum-to-one and non-negativity of the abundances, the nonnegativity of the nonlinearity coefficients, the spectral smoothness of the ME terms and the spatial sparseness of the residuals. The resulting convex problem is solved using the alternating direction method of multipliers (ADMM) whose convergence is ensured theoretically. The proposed mixture models and their unmixing algorithms are validated on both synthetic and real images showing competitive results regarding the quality of thenference and the computational
