Hyperspectral image quality evaluation using generalized regression neural network

作者：

Highlights：

• The features descriptive to hyperspectral images are collaboratively and seamlessly integrated.

• Consistency maps of hyperspectral images is consistent with the human visual perception system.

• A generalized regression neural network (GRNN) with a few parameters is applied to rank the quality of hyperspectral images.

摘要

•The features descriptive to hyperspectral images are collaboratively and seamlessly integrated.•Consistency maps of hyperspectral images is consistent with the human visual perception system.•A generalized regression neural network (GRNN) with a few parameters is applied to rank the quality of hyperspectral images.

论文关键词：Hyperspectral image,Feature extraction,GRNN,The phase-consistent map

论文评审过程：Received 30 June 2019, Revised 4 January 2020, Accepted 14 January 2020, Available online 16 January 2020, Version of Record 18 February 2020.

论文官网地址：https://doi.org/10.1016/j.image.2020.115785