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Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [article]

Junjun Jiang, He Sun, Xianming Liu, Jiayi Ma
2020 arXiv   pre-print
To exploit the spatial and spectral prior, we design a spatial-spectral block (SSB), which consists of a spatial residual module and a spectral attention residual module.  ...  Considering that the hyperspectral training samples are scarce and the spectral dimension of hyperspectral image data is very high, it is nontrivial to train a stable and effective deep network.  ...  Recently, low-resolution hyperspectral image and highresolution multispectral image fusion based spatial resolution improvement technique, which is often referred as hyperspectral image super-resolution  ... 
arXiv:2005.08752v1 fatcat:pt3whdmj2fanpckezab6gf2xru

Learning Based Super Resolution Application for Hyperspectral Images

Hüseyin AYDİLEK, Nihat İNANÇ
2021 International scientific and vocational studies journal  
First the application obtains a super-resolution image from a single hyperspectral image with a low spatial image with a deep convolutional neural network.  ...  In this paper, a hybrid application based on deep learning and sparse representation is applied to increase the low spatial resolution of hyperspectral images.  ...  Hu, Li, and Xie (2017) propose a hybrid spatial error correction model and a deep spectral difference convolutional neural network combination model for hyperspectral super-resolution.  ... 
doi:10.47897/bilmes.1049338 fatcat:wayekvbxkngn7mn4yhwxvocpqi

Curvelet based hyperspectral image fusion

Sha Wang, Hua-jun Feng, Zhi-hai Xu, Qi Li, Yue-ting Chen, Lifu Zhang, Jianfeng Yang
2013 International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications  
Hyperspectral image fusion (HIF) reconstructs high spatial resolution hyperspectral images from low spatial resolution hyperspectral images and high spatial resolution multispectral images.  ...  We propose a method for blind HIF problem based on deep learning, where the estimation of the observation model and fusion process are optimized iteratively and alternatingly during the super-resolution  ...  Deep Blind Iterative Fusion Network (DBIN) Using the above algorithm, we create a deep neural network for HSI fusion by unfolding all steps of the algorithm as network layers.  ... 
doi:10.1117/12.2031476 fatcat:ys2iofucm5dfdoss5njwiv3y44

Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [article]

Qi Wang, Qiang Li, Xuelong Li
2020 arXiv   pre-print
To address this issue, in this paper, we propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet).  ...  Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently.  ...  The authors are with the School of Computer Science and the Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, China (e-mail: crabwq@gmail.com  ... 
arXiv:2001.04609v1 fatcat:53rr2i23lfgz3gshwx7htpog7q

Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation [article]

Xiuheng Wang, Jie Chen, Qi Wei, Cédric Richard
2021 arXiv   pre-print
Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution.  ...  Specifically, a novel, yet effective two-stream fusion network is designed to serve as a regularizer for the fusion problem.  ...  However, the high spectral resolution of hyperspectral images need to make a compromise with spatial resolution to ensure an acceptable signal-to-noise ratio (SNR) [5] .  ... 
arXiv:2009.04237v2 fatcat:cnfpwuoi3zfg3nbgxktqh3lieu

Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery [article]

Zhenjie Tang, Qing Xu, Zhenwei Shi, Bin Pan
2022 arXiv   pre-print
To deal with this issue, we propose a novel Feedback Refined Local-Global Network (FRLGN) for the super-resolution of hyperspectral image.  ...  With the development of deep learning technology, multi-spectral image super-resolution methods based on convolutional neural network have recently achieved great progress.  ...  Based on the number of input images, the HSI super-resolution methods can be roughly divided into fusion-based HSI super-resolution [6] - [8] and single HSI super-resolution [9] - [11] .  ... 
arXiv:2103.04354v2 fatcat:peniedkflrcyxk33oxfmjlv724

Hyperspectral Image Super-Resolution with 1D–2D Attentional Convolutional Neural Network

Jiaojiao Li, Ruxing Cui, Bo Li, Rui Song, Yunsong Li, Qian Du
2019 Remote Sensing  
Hyperspectral image (HSI) super-resolution (SR) is of great application value and has attracted broad attention.  ...  The hyperspectral single image super-resolution (HSISR) task is correspondingly difficult in SR due to the unavailability of auxiliary high resolution images.  ...  published before, the founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish  ... 
doi:10.3390/rs11232859 fatcat:btvw2fyv4vbs7oqzwjrj6n2uj4

SSF-CNN: Spatial and Spectral Fusion with CNN for Hyperspectral Image Super-Resolution

Xian-Hua Han, Boxin Shi, YinQiang Zheng
2018 2018 25th IEEE International Conference on Image Processing (ICIP)  
images and increasing the feature sizes of the hyperspectral image for fusion.  ...  Motivated by the great success of deep convolutional neural network (DCNN) in many computer vision tasks, this study aims to design a novel DCNN architecture for effectively fusing the LR hyperspectral  ...  A pilot study proposed a simple spatial and spectral fusion CNN (SSF-CNN) [20] , which adopted three convolutional layers based on the the SRCNN model for natural image super resolution.  ... 
doi:10.1109/icip.2018.8451142 dblp:conf/icip/HanSZ18 fatcat:hftmmszdyzenhdodmtmf3km27y

Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner

Jianhao Gao, Jie Li, Menghui Jiang
2021 Remote Sensing  
Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral  ...  An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion.  ...  One mainstream strategy to obtain high-resolution hyperspectral (HR HSI) optical images is to fuse the spectral information from low-resolution hyperspectral (LR HSI) images and spatial information from  ... 
doi:10.3390/rs13163226 fatcat:izn7xzbmtnapbiep5wdcaxbbby

PRISMA spatial resolution enhancement by fusion with Sentinel-2 data

Nicola Acito, Marco Diani, Giovanni Corsini
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Finally, the two images at different spatial resolutions are properly combined in order to obtain the super-resolved hyperspectral image.  ...  The first step of the PRISMA-SR procedure consists in fusing S2 data at different spatial resolutions to obtain a synthetic MS image with 10 m spatial resolution and 10 spectral bands.  ...  ACKNOWLEDGMENT This project is carried out using PRISMA products, © of the Italian Space Agency (ASI), delivered under an ASI License to use.  ... 
doi:10.1109/jstars.2021.3132135 fatcat:ulc4cxazyrcbxo5ht4s6bbe42m

Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution

Qiang Li, Qi Wang, Xuelong Li
2020 Remote Sensing  
To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution.  ...  Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12101660 fatcat:dwbplbfdc5hbjfpmk32iltwg4i

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Li and Y. Zhang 2307 Hybrid 2-D-3-D Deep Residual Attentional Network With Structure Tensor Constraints for Spectral Super-Resolution of RGB Images ...................................................  ...  Liu 2245 Super-Resolution Mapping Based on Spatial-Spectral Correlation for Spectral Imagery .................................. .........................................................................  ... 
doi:10.1109/tgrs.2021.3052119 fatcat:obk5h6sp2nh47ounq4jqlhukcu

HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images

Zhan Shi, Chang Chen, Zhiwei Xiong, Dong Liu, Feng Wu
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Hyperspectral recovery from a single RGB image has seen a great improvement with the development of deep convolutional neural networks (CNNs).  ...  We first develop a deep residual network named HSCNN-R, which comprises a number of residual blocks.  ...  Acknowledgments This work is partially supported by the Natural Science Foundation of China under grants 61671419 and 61425026.  ... 
doi:10.1109/cvprw.2018.00139 dblp:conf/cvpr/ShiCXLW18 fatcat:bu3yujyiyvd3dhlddkjkbzrcxi

Research on super-resolution reconstruction of remote sensing images: a comprehensive review

Hui Liu, Yurong Qian, Xiwu Zhong, Long Chen, Guangqi Yang
2021 Optical Engineering: The Journal of SPIE  
The super-resolution (SR) reconstruction of remote sensing images is a low-cost and efficient method to improve their resolution, and it is often used for further image analysis.  ...  , and deep-learning-based methods.  ...  al. 80 used a deep hyperspectral prior and a dual-attention residual network for multi-frame SR reconstruction with fusion of hyperspectral and panchromatic images.  ... 
doi:10.1117/1.oe.60.10.100901 fatcat:44ssaq55ebfkrghyatvo5lbr2m

Special Section Guest Editorial: Representation Learning and Big Data Analytics for Remote Sensing

Weifeng Liu, Yicong Zhou, Karen Panetta, Sos Agaian
2020 Journal of Applied Remote Sensing  
Junwei Zhang et al. presented a satellite image super-resolution based on progressive residual deep neural network.  ...  Daizhi Kuang and Juncai Xu combined multiple spectral-spatial features and multikernel support tensor machine for hyperspectral image classification.  ...  Great appreciations to the reviewers and the Journal of Applied Remote Sensing editorial team for conducting a high-quality review process for all of the published papers.  ... 
doi:10.1117/1.jrs.14.032601 fatcat:635nbmh6ojefljv4k3uhxayrmi
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