Automated Cloud Detection Algorithm for Multi-spectral High Spatial Resolution Images Using Landsat-8 OLI

In this paper, an automatic and efficient cloud detection algorithm for multi-spectral high spatial resolution images is proposed. Based on the statistical properties and spectral properties on a large number of the imagery with cloud layers, a multispectral-based progressive optimal scheme for detecting clouds in Landsat-8 imagery is presented. First, a basic process which distinguishes the difference between cloud regions and non-cloud regions is constructed. Based on the spectral properties of cloud and the optimal threshold setting, we obtain a basic cloud detection result which separates the input imagery into the potential cloud pixels and non-cloud pixels. Then, the potential cloud regions and the cloud optimal map are used together to derive the potential cloud layer. An optimal process of probability for clouds over land and water is implemented with a combination of a normalized snow/ice inspection and spectral variability inspection. Finally, in order to obtain the accurate cloud regions from the potential cloud regions, a robust refinement process derived from a guided filter is constructed to guide us in removing non-cloud regions from the potential cloud regions. The boundaries of cloud regions and semi-transparent cloud regions are further marked to achieve the final cloud detection results. The proposed algorithm is implemented on the Landsat-8 imagery and evaluated in visual comparison and quantitative evaluation, and the cloud-covered regions were effectively detected without manual intervention.