Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network

The spectral super-resolution of multispectral image (MSI) refers to improving the spectral resolution of the MSI to obtain the hyperspectral image (HSI). Most recent works are based on the sparse representation to unfold the MSI into the 2-D matrix in advance for subsequent operations, which result...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
Main Authors: Zheng, Xiangtao, Chen, Wenjing, Lu, Xiaoqiang
Format: Article
Language:English
Subjects:
Attention
Convolution
convolutional neural networks
Dictionaries
Feature extraction
hyperspectral image (HSI)
Hyperspectral imaging
Image reconstruction
Image resolution
Mapping
multispectral image (MSI)
Reconstruction
Resolution
Spatial data
spatial–spectral features
Spectra
Spectral bands
Spectral resolution
spectral super-resolution
Superresolution
Task analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The spectral super-resolution of multispectral image (MSI) refers to improving the spectral resolution of the MSI to obtain the hyperspectral image (HSI). Most recent works are based on the sparse representation to unfold the MSI into the 2-D matrix in advance for subsequent operations, which results in that the spatial information of MSI cannot be fully explored. In this article, a spatial-spectral residual attention network (SSRAN) is proposed to simultaneously explore the spatial and spectral information of MSI for reconstructing the HSI. The proposed SSRAN is composed of the feature extraction part, the nonlinear mapping part, and the reconstruction part. Firstly, the multispectral features of the input MSI are extracted in the feature extraction part. Second, in the nonlinear mapping part, the spatial-spectral residual blocks are proposed to explore spatial and spectral information of MSI for mapping the multispectral features to the hyperspectral features. Finally, in the reconstruction part, a 2-D convolution is used to reconstruct the HSI from the hyperspectral features. Also, a neighboring spectral attention module is specially designed to explicitly constrain the reconstructed HSI to maintain the correlation among neighboring spectral bands. The proposed SSRAN outperforms the state-of-the-art methods on both simulated and real databases.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3104476