Hyperspectral Image Classification by Exploiting the Spectral-Spatial Correlations in the Sparse Coefficients

摘要：

　　This paper proposes a novel hyperspectral image (HSI) classification method based on sparse model,which incorporates the spectral and spatial information of the sparse coefficient.Firstly,a sparse dictionary is built by using the training samples and the sparse coefficient is obtained through the sparse representation method.Secondly,a probability map for each class is established by summing the sparse coefficients of each class.Thirdly,the mean filtering is applied on each probability map to exploit the spatial information.Finally,we compare the probability map to find the maximum probability for each pixel and then determine the class label of each pixel.Experimental results demonstrate the effectiveness of the proposed method.

关键词： Hyperspectral image classification sparse representation spectral-spatial information mean filter

作者: Dan Liu Shutao Li Leyuan Fang

作者单位: College of Electrical and Information Engineering,Hunan University,Changsha,410012,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 151-158

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）