Regularized Sparse Band Selection via Learned Pairwise Agreement

Zhixi Feng, Shuyuan Yang, Xiaolong Wei, Quanwei Gao, Licheng Jiao
2020 IEEE Access  
Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement. In RBS-LPA, a spatial-spectral informative
more » ... ph, constructed by spatial-spectral neighbor relationship, is incorporated to encode both the spatial and spectral geometrical structure. By combining the learning procedure with graph regularizer jointly, the graph regularizer can adaptively change with the sparse representative bands, which ensures that the selected bands can well preserve the local manifold structure. Experimental results on three real urban hyperspectral data demonstrate the efficiency of the proposed RBS-LPA and achieve convincing performance. INDEX TERMS Feature selection, self-representative learning, sparse optimization, graph regularizer. 40096 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020
doi:10.1109/access.2020.2971556 fatcat:72u7qlvq75cpnatvv773xlpxcq