报告人：彭江涛 副教授（湖北大学）

报告题目：Self-Paced Joint Sparse Representation for the Classification of Hyperspectral Images

报告摘要：In this paper, a self-paced joint sparse representation (SPJSR) model is proposed for the classification of hyperspectral images (HSIs). It replaces the least-squares (LS) loss in the standard joint sparse representation (JSR) model with a weighted LS loss and adopts a self-paced learning (SPL) strategy to learn the weights for neighboring pixels. Rather than predefining a weight vector in the existing weighted JSR methods, both the weight and sparse representation (SR) coefficient associated with neighboring pixels are optimized by an alternating iterative strategy. According to the nature of SPL, in each iteration, neighboring pixels with nonzero weights (i.e., easy pixels) are included for the joint SR of a testing pixel. With the increase of iterations, the model size (i.e., the number of selected neighboring pixels) is enlarged and more neighboring pixels from easy to complex are gradually added into the JSR learning process. After several iterations, the algorithm can be terminated to produce a desirable model that includes easy homogeneous pixels and excludes complex inhomogeneous pixels. Experimental results on two benchmark hyperspectral data sets demonstrate that our proposed SPJSR is more accurate and robust than existing JSR methods, especially in the case of heavy noise.

报告人简介：彭江涛，男，博士，湖北大学数学与统计学学院副教授。2011年7月获中国科学院自动化研究所工学博士学位。2013年8月至2014年7月应邀访问澳门大学，2017年9月至2018年9月公派出国访问密西西比州立大学Qian Du教授。多次参加国际国内学术会议。自2008年开始从事模式识别和遥感图像处理相关的研究工作，在高光谱图像的特征提取和分类等方面进行了较为深入的研究，在遥感图像处理领域权威期刊IEEE TGRS, IEEE JSTARS, IEEE GRSL 等期刊发表SCI论文20余篇；主持国家自然科学基金项目2项、湖北省自然科学基金1项。担任SCI期刊Remote Sensing、Applied Sciences的客座编辑（Guest Editor）。

报告时间：2018年10月9日（星期二）上午10:30-11:30

报告地点：科技楼南楼602室