Spectral-Spatial Classification of Hyperspectral Image Based on Discriminant Sparsity Preserving Embedding
作者: Min HanChengkun Zhang
作者单位: 1Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
刊名: Neurocomputing, 2017
来源数据库: Elsevier Journal
DOI: 10.1016/j.neucom.2017.03.009
关键词: Hyperspectral imageGraph-embedding frameworkSparse graph constructionSparse representation
原始语种摘要: Abstract(#br)The last few years have witnessed the success of sparse representation in hyperspectral image classification. However, the high computational complexity brings some worries to its applications. In this paper, a novel sparse representation based feature extraction algorithm, called discriminant sparsity preserving embedding (DSPE), is proposed by constructing a sparse graph and applying it to the graph-embedding framework. The proposed algorithm encodes supervised information mainly in stage of sparse graph construction, in which only the training samples in the same class are used to calculated the reconstructive coefficients during sparse reconstruction. An approach combining l 1 -norm and l 2 -norm is applied to solve the reconstruction weights, where l 1 -norm ensures the...
全文获取路径: Elsevier  (合作)
影响因子:1.634 (2012)

  • reconstruction 复原
  • graph 图表
  • weight 
  • framework 构架
  • discriminant 判别式
  • image 
  • algorithm 算法
  • sparse 稀疏的
  • computational 计算的
  • preserving 保藏