报告名称：Tensor Subspace Learning and Classification: TLDE for HSI classification

主办单位：英国立博官网中文版

报告专家：赵丽娜

专家所在单位：北京化工大学

报告时间：2020年9月25日09：00

报告地点：腾讯会议（会议ID：336 711 280）

专家简介：赵丽娜，北京化工大学数理学院副教授，硕士生导师。2004年7月在中国科学院数学与系统科学研究院获理学博士学位；2011年9月-2012年8月在美国加州大学欧文分校访问。主要从事计算与应用数学的交叉科学和数据科学的研究。主持国家自然科学基金多项，在国内外专业学术杂志上发表多篇SCI科研论文。

报告摘要：Hyperspectral image (HSI) has shown promising results in many fields because of its high spectral resolution. However, the redundancy of spectral dimension seriously affects the classification of HSI. Therefore, many popular dimension reduction (DR) algorithms are proposed and subspace learning algorithm is a typical one. In HSI, cube data is traditionally flatted into 1-D vector, so spatial information is completely ignored in most dimension reduction algorithms. The tensor representation for HSI considers both the spatial information and cubic properties simultaneously, so that tensor subspace learning can be naturally introduced into DR for HSI. In this paper, a tensor local discriminant embedding (TLDE) is proposed for DR and classification of HSI. TLDE can take full advantage of spatial structure and spectral information and map a high dimensional space into a low dimensional space by three projection matrices trained. TLDE can be more discriminative by calculating an intrinsic graph and a penalty graph. The experimental results on two real datasets demonstrate that TLDE is effective and works well even when the training samples are small.

邀请人：刘慧清