High-quality spectral-spatial reconstruction using saliency detection and deep feature enhancement

Highlights：

• We promote an adaptive weighting method based on structure tensor that considers the contribution rate of each spectral band.

• We propose a novel method to extract spatial details from an HSI that is more appropriate in terms of the significance of its physics and avoids spectral distortion.

• We propose a deep saliency enhancement method that uses CNN to learn the feature representation from input pixels and is trained in an end-to-end manner.

• We utilize the NMF algorithm to extract the spatial features provided by the enhanced saliency and spectral features provided by the original HSI and then merge these features to obtain a high-quality HSI.

摘要

•We promote an adaptive weighting method based on structure tensor that considers the contribution rate of each spectral band.•We propose a novel method to extract spatial details from an HSI that is more appropriate in terms of the significance of its physics and avoids spectral distortion.•We propose a deep saliency enhancement method that uses CNN to learn the feature representation from input pixels and is trained in an end-to-end manner.•We utilize the NMF algorithm to extract the spatial features provided by the enhanced saliency and spectral features provided by the original HSI and then merge these features to obtain a high-quality HSI.

论文关键词：Hyperspectral image,Quality enhancement,Structure tensor,Deep neural networks,Adaptive weighting,Nonnegative matrix factorization

论文评审过程：Received 3 April 2018, Revised 20 September 2018, Accepted 9 November 2018, Available online 20 November 2018, Version of Record 22 November 2018.