Deep Hyperspectral Image Sharpening

By Renwei Dian, Shutao Li, Anjing Guo, Leyuan Fang. IEEE Transactions on Neural Networks and Learning Systems, 2018

360 citations recorded by the source corpus.

Hyperspectral image (HSI) sharpening, which aims at fusing an observable low spatial resolution (LR) HSI (LR-HSI) with a high spatial resolution (HR) multispectral image (HR-MSI) of the same scene to acquire an HR-HSI, has recently attracted much attention. Most of the recent HSI sharpening approaches are based on image priors modeling, which are usually sensitive to the parameters selection and time-consuming. This paper presents a deep HSI sharpening method (named DHSIS) for the fusion of an LR-HSI with an HR-MSI, which directly learns the image priors via deep convolutional neural network-based residual learning. The DHSIS method incorporates the learned deep priors into the LR-HSI and HR-MSI fusion framework. Specifically, we first initialize the HR-HSI from the fusion framework via solving a Sylvester equation. Then, we map the initialized HR-HSI to the reference HR-HSI via deep residual learning to learn the image priors. Finally, the learned image priors are returned to the fusion framework to reconstruct the final HR-HSI. Experimental results demonstrate the superiority of the DHSIS approach over existing state-of-the-art HSI sharpening approaches in terms of reconstruction accuracy and running time.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/d5ce8f2c3c4be1fbae610a4b81eee85c41337150

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Citations in the corpus

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, background): “com/TaoHuang95/DHIF-Net DHSIS (Dian et al., 2018) MATLAB+Keras https://github.”
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, background): “Compared with hyperspectral imaging sensors, the existing multispectral imaging sensors capture MSIs with much higher spatial resolution and SNR [8].”
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, methodology): “Therefore, HSIs have gained more and more usage in a lot of earth remote sensing tasks [1], [2], including object classiﬁcation [3]–[7] and change detection [8].”
Lightweight Pyramid Networks for Image Deraining (2018, methodology): “Learning-based methods and convolutional neural networks (CNN) have proven useful for a variety of high-level vision tasks [16]–[23], as well as various image processing problems [24]–[28].”