Name
Abstract | ||
|---|---|---|
In recent years, deep learning-based remote-sensing image super-resolution (SR) methods have made significant progress, and these methods require a large number of synthetic data for training. To obtain sufficient training data, researchers often generate synthetic data via fixed bicubic downsampling methods. However, the synthesized data cannot reflect the complex degradation process of real remote-sensing images. Thus, performance will dramatically reduce when these methods work in real low-resolution (LR) remote-sensing images. This letter proposes a U-shaped attention connection network (US-ACN) for remote-sensing image SR to solve this issue. Our US-ACN does not rely on any synthetic external dataset for training and merely requires one LR image to complete the training. The US-ACN utilizes remote-sensing images' strong internal feature repetitiveness and fully learns this internal repetitive feature through a well-designed US-ACN to achieve the remote-sensing image SR. In addition, we design a 3-D attention module to generate effective 3-D weights by modeling channel and spatial attention weights, which is more helpful for the learning of internal features. Through the U-shaped connection among attention modules, context information propagation and attention weights learning are fully utilized. Many experiments show that our US-ACN adequately adapts to the remote-sensing image SR in various situations and performs advanced performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3127988 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Remote sensing, Training, Superresolution, Feature extraction, Solid modeling, Sensors, Decoding, Attention connection, image super-resolution (SR), internal feature, remote-sensing image, U-shaped | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenzong Jiang | 1 | 0 | 0.68 |
| Lifei Zhao | 2 | 0 | 1.01 |
| Yanjiang Wang | 3 | 15 | 8.65 |
| WeiFeng Liu | 4 | 59 | 11.72 |
| Bao-Di Liu | 5 | 166 | 27.34 |