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Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network
| Content Provider | MDPI |
|---|---|
| Author | Ma, Yunchuan Lv, Pengyuan Liu, Hao Sun, Xuehong Zhong, Yanfei |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 2966 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13152966 |
| Journal | Remote Sensing |
| Issue Number | 15 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-28 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Remote Sensing Images Super Resolution Dense Network Attention Mechanism |
| Content Type | Text |
| Resource Type | Article |
