Cloud Detection of Optical Remote Sensing Image Time Series Using P-norm based Regression Model

An automatic multi-temporal method is proposed in this paper for cloud detection without known the reference image in prior. A series of reference images are provided by fitting robustly of the pixels of multi-temporal images contaminated by clouds to show the inherent gradual change of the landscape with time instants. Then the cloud is detected by thresholding the difference between the target and the reference images, which is found to be merely composed of the regression model error modeled as Gaussian noise and outliers corresponding to cloud and its shadow. The proposed method is compared with state-of-the-art algorithms on the LANDSAT dataset, and shows a better discrimination of cloud and cloud shadow covered pixels from the uncontaminated ones.