Name
Abstract | ||
|---|---|---|
The optical remote sensing images not only have to make a fundamental tradeoff between the spatial and spectral resolutions, but also are inevitable to be polluted by the clouds; however, the existing pansharpening methods mainly focus on the resolution enhancement of the optical remote sensing images without cloud contamination. How to fuse the cloud-contaminated images to achieve the joint resolution enhancement and cloud removal is a promising and challenging work. In this paper, a pansharpening method for the challenging cloud-contaminated very high-resolution remote sensing images is proposed. Furthermore, the cloud-contaminated conditions for the practical observations with all the thick clouds, the thin clouds, the haze, and the cloud shadows are comprehensively considered. In the proposed methods, a two-step fusion framework based on multisource and multitemporal observations is presented: 1) the thin clouds, the haze, and the light cloud shadows are proposed to be first jointly removed and 2) a variational-based integrated fusion model is then proposed to achieve the joint resolution enhancement and missing information reconstruction for the thick clouds and dark cloud shadows. Through the proposed fusion method, a promising cloud-free fused image with both high spatial and high spectral resolutions can be obtained. To comprehensively test and verify the proposed method, the experiments were implemented based on both the cloud-free and cloud-contaminated images, and a number of different remote sensing satellites including the IKONOS, the QuickBird, the Jilin (JL)-1, and the Deimos-2 images were utilized. The experimental results confirm the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TGRS.2018.2878007 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Remote sensing,Spatial resolution,Radiometry,Optical imaging,Optical sensors,Satellites | Computer vision,Satellite,Remote sensing,Radiometry,Artificial intelligence,Fuse (electrical),Image resolution,Optical imaging,Mathematics,Haze,Cloud computing | Journal |
Volume | Issue | ISSN |
57 | 5 | 0196-2892 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangchao Meng | 1 | 10 | 5.20 |
| Huanfeng Shen | 2 | 1394 | 84.63 |
| Qiangqiang Yuan | 3 | 583 | 41.52 |
| Huifang Li | 4 | 62 | 8.11 |
| Liangpei Zhang | 5 | 94 | 16.55 |
| Weiwei Sun | 6 | 94 | 20.80 |