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Cloud detection of multi-feature remote sensing images based on deep learning
| Content Provider | Scilit |
|---|---|
| Author | Yao, Jiaqi Zhai, Haoran Wang, Guanghui |
| Copyright Year | 2021 |
| Description | Journal: Iop Conference Series: Earth and Environmental Science Satellite remote sensing technology provides massive image data for meteorological field, which can accurately extract the spatial distribution of cloud, and can be used to analyze the spatial and temporal changes of cloud. Aiming at the problems that the traditional algorithm is sensitive to noise and has poor recognition effect on broken clouds, a multi-feature remote sensing image cloud detection algorithm based on neural network structure is proposed, which has a good recognition effect on all types of clouds. The experimental results show that the accuracy of cloud detection in this paper reaches 92%, which is about 6% higher than the traditional algorithm, and avoids the disadvantage of the traditional algorithm which is sensitive to noise. |
| Related Links | https://iopscience.iop.org/article/10.1088/1755-1315/687/1/012155/pdf |
| ISSN | 17551307 |
| e-ISSN | 17551315 |
| DOI | 10.1088/1755-1315/687/1/012155 |
| Journal | Iop Conference Series: Earth and Environmental Science |
| Issue Number | 1 |
| Volume Number | 687 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2021-03-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Earth and Environmental Science Remote Sensing Cloud Detection Traditional Algorithm Recognition Effect |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Physics and Astronomy Environmental Science |
