Hierarchy Clustering for Cloud Detection Assisted by Spectral Features of Ground Covers

Cloud detection is an important procedure for the processing of remote sensing images. A cloud detection scheme driven by the spectral and the temporal features is presented in this paper, where an unsupervised hierarchy clustering approach is proposed for large scale image segmentation. The potential cloudy pixels are identified by means of the spectral matching, in which the spectral data of the clustering centers are compared to the patterns in the spectral dataset of ground covers. The matched pixels are regarded as cloudless pixels, whose category can be recognized accordingly. In contrast, the bright temperatures corresponding to the unmatched pixels are used to exclude the interference of the occasional hotspots, enabling the final cloud detection result. Landsat 8, Sentinel-2, and MODIS satellite data are used in the validation to demonstrate the precision and stability of the proposed scheme for the data at different spatial resolutions.