校园网讯   日前，由信息学院图像信息处理与智能系统校级科研创新团队张国云教授、涂兵副教授、吴健辉教授等人指导的2017级硕士研究生张晓飞、王锦萍撰写的论文“Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation”被遥感领域国际权威期刊IEEE Geoscience and Remote Sensing Letters(IEEE GRS Letters)录用，目前该SCI期刊影响因子IF=2.761，是国际公认的IEEE旗下遥感领域三大期刊之一。

该论文在基于联合稀疏表示的高光谱遥感图像分类方面取得了新的研究进展，在联合稀疏表示理论基础上融合非局部子空间相关系数度量信息，提出了一种高光谱遥感图像分类新算法。

张晓飞本科毕业于我校信息与通信工程学院自动化专业，曾获得2016年中国电信奖学金暨“践行社会主义核心价值观先进个人”、国家励志奖学金、国家一等助学金、2017年湖南省优秀毕业生、2015年湖南理工学院“十佳实践创新个人”等20余项校级以上荣誉称号，本科期间在国际、国内CSCD期刊上发表学术论文2篇。王锦萍本科也毕业于我校信息与通信工程学院自动化专业，曾获得国家励志奖学金、2017年湖南大学暑期学校“优秀学员”, 2017年岳阳市优秀毕业生、2017年优秀毕业设计、2016年湖南理工学院“十佳实践创新个人”等20余项校级以上荣誉称号，本科期间在国内CSCD期刊上发表论文3篇。（信息学院  责编 陈绪清）

附：论文摘要内容

Joint sparse representation based classifier assumes that pixels in a local window can be jointly and sparsely represented by a dictionary constructed by the training samples. The class label of each pixel can be decided according to the representation residual. However, once the local window of each pixel includes pixels from different classes, the performance of the JSR classifier may be seriously decreased. Since correlation coefficient (CC) is able to measure the spectral similarity among different pixels efficiently, this letter proposes a new classification method via fusing correlation coefficient and joint sparse representation (CCJSR), which attempts to use the within class similarity between training and test samples while decrease the between-class interference. First, the correlation coefficients among the training and test samples are calculated. Then, the sparse representation based classifier is used to obtain the representation residuals of different pixels. Finally, a regularization parameterλis introduced to achieve the balance between the JSR and the CC. Experimental results obtained on the Indian Pines data set demonstrate the competitive performance of the proposed approach with respect to other widely used classifiers。