Hyperspectral image quality evaluation using generalized regression neural network
作者: Rui HouYang HuYunHao ZhaoHuan Liu
作者单位: 1School of Economics and Management, North China Electric Power University, Beijing, 102206, PR China
2School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, PR China
3State-owned Assets Supervision and Administration Commission, Ningxia, 750000, PR China
刊名: Signal Processing: Image Communication, 2020, Vol.83
来源数据库: Elsevier Journal
DOI: 10.1016/j.image.2020.115785
关键词: Hyperspectral imageFeature extractionGRNNThe phase-consistent map
原始语种摘要: Abstract(#br)In order to alleviate the overfitting problem caused by image quality evaluation (IQA) model learning under intolerably small dataset, this paper proposes a multi-feature fusion-based deep architecture for hyperspectral image quality assessment. First, eight key IQA-related features, which are descriptive to the mean noise of multi-band images, spatial correlation, inter-spectral correlation, blur, and the phase-consistent map of images, are extracted from each hyperspectral image within the dataset. Based on this, a carefully-designed generalized regression neural network (GRNN) with a limited number of parameters is hierarchically trained by the feature vectors from samples in the training IQA data set. Comprehensive experimental evaluations on the hyperspectral IQA images...
全文获取路径: Elsevier  (合作)
影响因子:1.286 (2012)

  • quality 品质
  • image 
  • evaluation 评价
  • generalized 广义
  • regression 海退
  • neural 神经系统的
  • reference 基准电压源
  • proposed 建议的
  • network 网络
  • descriptive 描述的