Name
Title | ||
|---|---|---|
Translution-SNet: A Semisupervised Hyperspectral Image Stripe Noise Removal Based on Transformer and CNN |
Abstract | ||
|---|---|---|
Hyperspectral remote sensing images (HSIs) have been applied in urban planning, environmental monitoring, and other fields. However, they are susceptible to noise interference, such as Gaussian noise, stripe, and mixed noises, from various factors in the imaging process, which greatly limits their applications. Although previous efforts to improve HSI quality have achieved remarkable results, there are still many challenges to be solved. To avoid the poor generalization ability and improve the stripe removal performance of the network in real scenarios, in this article, we proposed a novel deep learning model (Translution-SNet) for HSI stripe noise removal based on a semisupervised training strategy that applies a convolution and transformer for feature extraction. Moreover, we used an unbiased estimation method to calculate the loss function of the unsupervised part from noisy data without a clean image. The semisupervised method improved the ability of Translution-SNet to deal with various complex stripe noises during stripe removal and strengthened its robustness and generalization ability. Our experimental results showed that Translution-SNet could robustly handle stripe noise of images with different loads and achieve satisfactory results, proving its feasibility and effectiveness. In addition, Translution-SNet showed good generalization ability. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2022.3182745 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Transformers, Feature extraction, Data models, Training, Noise reduction, Task analysis, Deep learning, Hyperspectral image destriping, remote sensing, semisupervised, transformer, unbiased estimate | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengjun Wang | 1 | 0 | 1.01 |
| Miaozhong Xu | 2 | 0 | 1.01 |
| Yong-hua Jiang | 3 | 22 | 5.41 |
| Guo Zhang | 4 | 0 | 10.48 |
| Hao Cui | 5 | 2 | 3.62 |
| Litao Li | 6 | 0 | 0.34 |
| Da Li | 7 | 0 | 0.34 |