A kernel regression approach to cloud and shadow detection in multitemporal images

Earth observation satellites will provide in the next years time series with enough revisit time allowing a better surface monitoring. In this work, we propose a cloud screening and a cloud shadow detection method based on detecting abrupt changes in the temporal domain. It is considered that the time series follows smooth variations and abrupt changes in certain spectral features will be mainly due to the presence of clouds or cloud shadows. The method is based on linear and nonlinear regression analysis; in particular we focus on the regularized least squares and kernel regression methods. Experiments are carried out using Landsat 5 TM time series acquired over Albacete (Spain), and comparative results with the Fmask approach [1] show the potential of exploiting the temporal domain.