Hyperspectral Image Unmixing Based on Sparse and Minimum Volume Constrained Nonnegative Matrix Factorization

摘要：

　　Hyperspectal Unmixing (HU) aims at getting the endmember signature and their corresponding abundance maps from highly mixed Hyperspctral image.Nonnegative Matrix Factorization (NMF) is a widely used method for HU recently.Traditional NMF only take sparse constraint or minimum volume constraint into consideration leading to unmixing results not accurately enough.In this paper,we propose a new method based on NMF through combining volume constraint with sparse constraint.According to the convex geometry,we impose minimum volume constraint on endmember matrix.Because sparsity is nature property of abundance,we add the sparse constraint on abundance matrix.Both the experiments on synthetic and real scene images show the effectiveness of the proposed method.

关键词： Hyperspetral unmixing nonnegative matrix factorization minimum volume constraint sparse constraint

作者: Denggang Li Shutao Li Huali Li

作者单位: College of Electrical and Information Engineering,Hunan University,Changsha,410082,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: 44-52

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）