Next Article in Journal
An Under-Ice Hyperspectral and RGB Imaging System to Capture Fine-Scale Biophysical Properties of Sea Ice
Previous Article in Journal
Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression
Open AccessArticle

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

by Jiaojiao Li 1,2,†, Ruxing Cui 1,*, Bo Li 3,†, Rui Song 1,*, Yunsong Li 1,* and Qian Du 4,†
1
The State Key Lab. of Integrated Service Networks, Xidian University, Xi’an 710000, China
2
The CAS Key Laboratory of Spectral Imaging Technology, Xi’an 710119, China
3
The School of Electronic Information, Northwestern Polytechnical University, Xi’an 710000, China
4
The Department of Electronic and Computer Engineering, Mississippi State University, Mississippi, MS 39762, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(23), 2859; https://doi.org/10.3390/rs11232859
Received: 29 September 2019 / Revised: 18 November 2019 / Accepted: 28 November 2019 / Published: 1 December 2019
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. To tackle this challenging task, different from the existing learning-based HSISR algorithms, in this paper we propose a novel framework, i.e., a 1D–2D attentional convolutional neural network, which employs a separation strategy to extract the spatial–spectral information and then fuse them gradually. More specifically, our network consists of two streams: a spatial one and a spectral one. The spectral one is mainly composed of the 1D convolution to encode a small change in the spectrum, while the 2D convolution, cooperating with the attention mechanism, is used in the spatial pathway to encode spatial information. Furthermore, a novel hierarchical side connection strategy is proposed for effectively fusing spectral and spatial information. Compared with the typical 3D convolutional neural network (CNN), the 1D–2D CNN is easier to train with less parameters. More importantly, our proposed framework can not only present a perfect solution for the HSISR problem, but also explore the potential in hyperspectral pansharpening. The experiments over widely used benchmarks on SISR and hyperspectral pansharpening demonstrate that the proposed method could outperform other state-of-the-art methods, both in visual quality and quantity measurements. View Full-Text
Keywords: 1D–2D convolutional neural network; attentional; spatial–spectral; HSI; super-resolution 1D–2D convolutional neural network; attentional; spatial–spectral; HSI; super-resolution
Show Figures

Graphical abstract

MDPI and ACS Style

Li, J.; Cui, R.; Li, B.; Song, R.; Li, Y.; Du, Q. Hyperspectral Image Super-Resolution with 1D–2D Attentional Convolutional Neural Network. Remote Sens. 2019, 11, 2859.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop