Learning group-based sparse and low-rank representation for hyperspectral image classification
作者：
Highlights：
• Propose a GSLR method to learn structured dictionary for HSI.
• Apply fast super-pixel segmentation method to gain spatial groups.
• Add group-based sparse and low-rank regularizations for dictionary learning.
• Update representation matrix by IALM and dictionary by BCD.
• Classify representation matrices of test samples by linear SVM.
摘要
Highlights•Propose a GSLR method to learn structured dictionary for HSI.•Apply fast super-pixel segmentation method to gain spatial groups.•Add group-based sparse and low-rank regularizations for dictionary learning.•Update representation matrix by IALM and dictionary by BCD.•Classify representation matrices of test samples by linear SVM.
论文关键词：Classification,Hyperspectral image (HSI),Dictionary learning,Sparse representation,Low-rank representation
论文评审过程：Received 16 June 2015, Revised 22 February 2016, Accepted 13 April 2016, Available online 22 April 2016, Version of Record 2 September 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.04.009
