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

Fusing a low-resolution hyperspectral image with the corresponding high-resolution RGB image to obtain a high-resolution hyperspectral image is usually solved as an optimization problem with prior-knowledge such as sparsity representation and spectral physical properties as constraints, which have l...

Full description

Saved in:
Bibliographic Details
Published in2018 25th IEEE International Conference on Image Processing (ICIP) pp. 2506 - 2510
Main Authors Han, Xian-Hua, Shi, Boxin, Zheng, YinQiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Computer architecture
Feature extraction
hyperspectral image
Hyperspectral imaging
Spatial and spectral fusion CNN
Spatial resolution
SSF-CNN
super resolution
Online AccessGet full text

Cover

More Information
Summary:Fusing a low-resolution hyperspectral image with the corresponding high-resolution RGB image to obtain a high-resolution hyperspectral image is usually solved as an optimization problem with prior-knowledge such as sparsity representation and spectral physical properties as constraints, which have limited applicability. Deep convolutional neural network extracts more comprehensive features and is proved to be effective in upsampling RGB images. However, directly applying CNNs to upsample either the spatial or spectral dimension alone may not produce pleasing results due to the neglect of complementary information from both low resolution hyper spectral and high resolution RGB images. This paper proposes two types of novel CNN architectures to take advantages of spatial and spectral fusion for hyperspectral image superresolution. Experiment results on benchmark datasets validate that the proposed spatial and spectral fusion CNNs outperforms the state-of-the-art methods and baseline CNN architectures in both quantitative values and visual qualities
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451142