Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13152966 | REMOTE SENSING |
Keywords | DocType | Volume |
remote sensing images, super resolution, dense network, attention mechanism | Journal | 13 |
Issue | Citations | PageRank |
15 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunchuan Ma | 1 | 0 | 0.34 |
| Pengyuan Lv | 2 | 44 | 3.70 |
| Hao Liu | 3 | 113 | 10.67 |
| Xuehong Sun | 4 | 3 | 2.74 |
| Yanfei Zhong | 5 | 1044 | 90.58 |