Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network
In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this...
Saved in:
Published in | Remote sensing (Basel, Switzerland) Vol. 13; no. 15; p. 2966 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.08.2021
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images. The proposed method learns multi-level feature information and pays more attention to the important and useful regions in order to better reconstruct the final image. Specifically, we construct a dense channel attention mechanism (DCAM), which densely uses the feature maps from the channel attention block via skip connection. This mechanism makes better use of multi-level feature maps which contain abundant high-frequency information. Further, we add a spatial attention block, which makes the network have more flexible discriminative ability. Experimental results demonstrate that the proposed DCAN method outperforms several state-of-the-art methods in both quantitative evaluation and visual quality. |
---|---|
ISSN: | 2072-4292 2072-4292 |
DOI: | 10.3390/rs13152966 |