| Autor: |
Wang, Kaizheng, Fu, Yitong, Zhou, Shunzhen, Zhou, Ruohan, Wen, Gang, Zhou, Fangrong, Li, Lulu |
| Předmět: |
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| Zdroj: |
International Journal of Remote Sensing; Feb2024, Vol. 45 Issue 3, p930-953, 24p |
| Abstrakt: |
Accurate and reliable cloud detection is one of the crucial preprocessing steps in satellite remote sensing. This paper adopts the spectral data in different channels observed by Himawari-8 geostationary satellite and proposes a cloud detection algorithm based on K-means++ clustering with a sharpening convolutional module (C-Kmeans++). The analysis found that the sum of the reflectance of channels 3 and 4, the brightness temperature of channel 15 and the difference of brightness temperature of channels 7 and 14 can be used as clustering features of the K-means++ algorithm. Before the clustering operation, the RS image is sharpened using the Laplacian operator to enhance the precision in the detection of thin clouds. A proper k value (k = 7) is obtained by the sum of squared error and silhouette coefficient. To evaluate the C-Kmeans++, the comparison of detection results using K-means++, the multi-spectral threshold, the k-nearest neighbour and the Vgg16+U-Net was carried out. The visible light image and Cloud-Aerosol Lidar with Orthogonal Polarization product were used as reference labels. It is found that RR, ER, HR and KSS of C-Kmeans++ are best throughout the day. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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