Cloud removal in image time series through unmixing

This paper proposes a new cloud removal algorithm for scenes within a SITS based on the concept of spectral unmixing, which decomposes hyperspectral image elements into fractional abundances of reference spectra related to the materials present in a given scene. In the case of SITS, we consider a reference spectrum as a pixel which presents a characteristic evolution in time of its spectral features. While traditional methods aim at substituting the affected pixels with other ones taken from other scenes, the proposed algorithm allows exploiting the full spectral and temporal information of the SITS to synthesize the expected values below a cloudy area. This is done independently from the overall atmospheric interactions affecting a given image, and it is possible even if only one acquisition is available for a given period of time.