Hyperspectral image super-resolution combining with deep learning and spectral unmixing

作者：

Highlights：

• We propose a HSI super-resolution method without the auxiliary images.

• Our framework combines with deep residual convolutional network and spectral unmixing.

• The TV is used to impose constraints on the abundance matrix.

• The experimental shows that our method has good performance than the state-of-the art methods.

摘要

•We propose a HSI super-resolution method without the auxiliary images.•Our framework combines with deep residual convolutional network and spectral unmixing.•The TV is used to impose constraints on the abundance matrix.•The experimental shows that our method has good performance than the state-of-the art methods.

论文关键词：Hyperspectral image (HSI),Super-resolution,Deep residual convolutional neural network (DRCNN),Spectral unmixing,Total variation (TV) regularity

论文评审过程：Received 25 October 2019, Revised 12 March 2020, Accepted 12 March 2020, Available online 14 March 2020, Version of Record 18 March 2020.

论文官网地址：https://doi.org/10.1016/j.image.2020.115833