Cloud detection from Himawari-8 spectral images using K-means++ clustering with the convolutional module

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.