| Autor: |
Fangrong Zhou, Gang Wen, Yi Ma, Yutang Ma, Hao Pan, Hao Geng, Jun Cao, Yitong Fu, Shunzhen Zhou, Kaizheng Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Mathematical Biosciences and Engineering, Vol 20, Iss 12, Pp 21588-21610 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1551-0018 |
| DOI: |
10.3934/mbe.2023955?viewType=HTML |
| Popis: |
Accurate cloud detection is an important step to improve the utilization rate of remote sensing (RS). However, existing cloud detection algorithms have difficulty in identifying edge clouds and broken clouds. Therefore, based on the channel data of the Himawari-8 satellite, this work proposes a method that combines the feature enhancement module with the Gaussian mixture model (GMM). First, statistical analysis using the probability density functions (PDFs) of spectral data from clouds and underlying surface pixels was conducted, selecting cluster features suitable for daytime and nighttime. Then, in this work, the Laplacian operator is introduced to enhance the spectral features of cloud edges and broken clouds. Additionally, enhanced spectral features are input into the debugged GMM model for cloud detection. Validation against visual interpretation shows promising consistency, with the proposed algorithm outperforming other methods such as RF, KNN and GMM in accuracy metrics, demonstrating its potential for high-precision cloud detection in RS images. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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