Spectral–spatial hyperspectral image ensemble classification via joint sparse representation

作者：

Highlights：

• Ensemble learning problems are considered as sparse reconstruction problems.

• Incorporate the contextual neighborhood knowledge during the learning stage.

• Complete the selection of classifiers subset and obtain weights in one step.

• The new ensemble system using fewer classifiers not only yields better performance but also reduces computational burden in testing phase.

摘要

•Ensemble learning problems are considered as sparse reconstruction problems.•Incorporate the contextual neighborhood knowledge during the learning stage.•Complete the selection of classifiers subset and obtain weights in one step.•The new ensemble system using fewer classifiers not only yields better performance but also reduces computational burden in testing phase.

论文关键词：Classification,Ensemble learning,Hyperspectral imagery,Joint sparse recovery,Spatial correlation

论文评审过程：Received 17 July 2015, Revised 28 January 2016, Accepted 31 January 2016, Available online 6 February 2016, Version of Record 23 August 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.01.033