Deep Residual Dual-Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

A super-resolution (SR) reconstruction of remote sensing images is becoming a highly active area of research. With increasing upscaling factors, richer and more abundant details can progressively be obtained. However, in comparison with natural images, the complex spatial distribution of remote sens...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 13; no. 14; p. 2784
Main Authors Huang, Bo, He, Boyong, Wu, Liaoni, Guo, Zhiming
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2021
Subjects
Algorithms
Artificial neural networks
attention mechanism
Convolution
Feature extraction
Image reconstruction
Image resolution
Information flow
Information processing
Modules
Neural networks
Performance indices
Remote sensing
residual learning
Spatial distribution
super-resolution
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Summary:A super-resolution (SR) reconstruction of remote sensing images is becoming a highly active area of research. With increasing upscaling factors, richer and more abundant details can progressively be obtained. However, in comparison with natural images, the complex spatial distribution of remote sensing data increases the difficulty in its reconstruction. Furthermore, most SR reconstruction methods suffer from low feature information utilization and equal processing of all spatial regions of an image. To improve the performance of SR reconstruction of remote sensing images, this paper proposes a deep convolutional neural network (DCNN)-based approach, named the deep residual dual-attention network (DRDAN), which achieves the fusion of global and local information. Specifically, we have developed a residual dual-attention block (RDAB) as a building block in DRDAN. In the RDAB, we firstly use the local multi-level fusion module to fully extract and deeply fuse the features of the different convolution layers. This module can facilitate the flow of information in the network. After this, a dual-attention mechanism (DAM), which includes both a channel attention mechanism and a spatial attention mechanism, enables the network to adaptively allocate more attention to regions carrying high-frequency information. Extensive experiments indicate that the DRDAN outperforms other comparable DCNN-based approaches in both objective evaluation indexes and subjective visual quality.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13142784