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Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
[article]
2020
arXiv
pre-print
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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
2021
International scientific and vocational studies journal
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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
2013
International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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2020 pre-print arXiv:2009.04237v1 fatcat:2ams3ugzvbguplfukhvijzdghq
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]
2022
arXiv
pre-print
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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
2019
Remote Sensing
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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
2018
2018 25th IEEE International Conference on Image Processing (ICIP)
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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
2021
Remote Sensing
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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
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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
2020
Remote Sensing
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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
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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
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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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
2021
Optical Engineering: The Journal of SPIE
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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
2020
Journal of Applied Remote Sensing
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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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