Name
Title | ||
|---|---|---|
Cloud-Free Sea-Surface-Temperature Image Reconstruction From Anomaly Inpainting Network |
Abstract | ||
|---|---|---|
Sea-surface temperature (SST) images obtained by satellites contain noise and missing SSTs due to cloud covers. We propose a method for reconstructing denoised cloud-free SST images via deep-learning-based image inpainting. For denoizing, we use data-assimilation images to train a reconstruction network by considering the physical correctness of SSTs. For reconstruction stability, we introduce anomaly inpainting network, which does not directly complete missing SSTs but estimates the difference between the unobserved SSTs and the average SSTs. SSTs do not fluctuate much over a few days; thus, we can use recent average SSTs as a rough estimation of SSTs and can assume that the SST difference will be within a specific range. We conducted experiments to evaluate our method with satellite SST images and in situ SST data. The results indicate that our method with anomaly inpainting network qualitatively and quantitatively outperformed conventional SST image inpainting methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2021.3111649 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Image reconstruction, Clouds, Satellites, Ocean temperature, Data models, Spatiotemporal phenomena, Training, Deep learning, inpainting, sea-surface temperature (SST) | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nobuyuki Hirahara | 1 | 0 | 0.34 |
| Motoharu Sonogashira | 2 | 0 | 0.34 |
| Masaaki Iiyama | 3 | 17 | 14.23 |