Name
Title | ||
|---|---|---|
Thin Cloud Removal for Remote Sensing Images Using a Physical-Model-Based CycleGAN With Unpaired Data |
Abstract | ||
|---|---|---|
Thin cloud removal from remote sensing (RS) images is challenging. Recently, deep-learning-based methods have achieved excellent results using supervised training on paired image data. However, in practice, real paired image data are unavailable. Therefore, in this letter, we propose a novel thin cloud removal method, a physical-model-based CycleGAN (PM-CycleGAN), which can be trained using only unpaired data. The PM-CycleGAN training process comprises forward and backward loops. The forward loop first decomposes a cloudy image into a cloud-free image, thin cloud thickness map, and thickness coefficient using three generators. Then, it combines these three components using a physical model to reconstruct the original cloudy image to obtain the cycle consistency constraint. The backward loop first uses the physical model to synthesize a cloud-free image, thin cloud thickness map, and thickness coefficient into a cloudy image, which are then decomposed into the original three components using the three generators. Visual and quantitative comparisons against several state-of-the-art (SOTA) methods on a cloudy image dataset demonstrated the superiority of PM-CycleGAN. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3140033 | IEEE Geoscience and Remote Sensing Letters |
Keywords | DocType | Volume |
CycleGAN,physical model,remote sensing (RS) images,thin cloud removal,unpaired data | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zi | 1 | 0 | 0.34 |
| Fengying Xie | 2 | 182 | 15.33 |
| Xuedong Song | 3 | 0 | 0.34 |
| Zhiguo Jiang | 4 | 321 | 45.58 |
| Haopeng Zhang | 5 | 47 | 14.75 |