| Autor: |
Zhao, Yunze Yao, Jianwen Hu, Yaoting Liu, Yushan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Remote Sensing; Volume 15; Issue 12; Pages: 3066 |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs15123066 |
| Popis: |
Many hyperspectral image (HSI) super-resolution (SR) methods have been proposed and have achieved good results; however, they do not sufficiently preserve the spectral information. It is beneficial to sufficiently utilize the spectral correlation. In addition, most works super-resolve hyperspectral images using high computation complexity. To solve the above problems, a novel method based on a channel multilayer perceptron (CMLP) is presented in this article, which aims to obtain a better performance while reducing the computational cost. To sufficiently extract spectral features, a local-global spectral integration block is proposed, which consists of CMLP and some parameter-free operations. The block can extract local and global spectral features with low computational cost. In addition, a spatial feature group extraction block based on the CycleMLP framework is designed; it can extract local spatial features well and reduce the computation complexity and number of parameters. Extensive experiments demonstrate that our method achieves a good performance compared with other methods. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
