报告人：李红教授

报告题目：Integral Probability Metric based Autoencoder for Hyperspectral Unmixing

报告摘要：

(1) Hyperspectral unmixing is a significant task in the remote sensing image analysis. In this talk, a joint metric neural network (JMnet) is proposed for hyperspectral unmixing, by introducing Wasserstein distance and feature matching as regularization terms, and SAD as the underlying loss. The proposed neural network consists of two parts, an autoencoder is used for endmember extraction and abundance estimation while a discriminator to compute the Wasserstein distance. The Wasserstein distance can stably provide useful gradient information that promotes the autoencoder to reach a solution with better unmixing performance.

(2) The feature matching is adopted to an intermediate layer of the discriminator for enforcing the features of the observation and the reconstruction to be equal, which can lead to further improvement of the unmixing performance. Model analysis and regularization parameter analysis are conducted to demonstrate the effectiveness of our method. Experimental results on four real-world hyperspectral data sets show that our method outperforms the state-of-the-art methods, especially in terms of abundance estimation.

报告时间：2022年10月29日上午10:00-13:00

报告形式：腾讯会议；会议号：755-896-048

获取会议密码请发邮件至：yaoli0508@hit.edu.cn

报告人简介：李红，二级教授，博士生导师，湖北省名师，华中卓越学者特聘教授及享受国务院政府特殊津贴专家。科技部国际科技合作计划评议专家，美国IEEE会员。主要从事逼近与计算、机器学习与模式识别等方面的研究，在IEEE Trans等重要学术期刊上发表学术论文60余篇。主持国家自然科学基金、“十二五”航天支撑计划项目及国防预研基金等多个科研项目。2006年至2022年期间多次应邀访问香港浸会大学、澳门大学、美国加州大学尔湾分校（UCI）、澳大利亚悉尼大学等，十余次出席国际学术会议。2006年获宝钢教育基金“优秀教师”奖；2009年主持建设的“复变函数与积分变换”课程被评为国家精品课程、2016年评为国家精品资源共享课程、 2018年评为国家精品在线开放课程及2020年被评为国家一流课程。