Seasonal Vegetation Change Detection Using Independent Component Analysis

The seasonal analysis of vegetation can be considered as looking for fundamental redundant information and detecting, at the same time, the natural changes of the vegetative cover undergone by the observed scene. From the statistical point of view, the redundant information can be quantified by the correlation coefficients between the multi-temporal images while the natural changes can be considered as the mutual information between the transition zones of the observed scene. For detecting and emerging the zones of transition and preserving at the same time the zones of vegetation temporal evolution stability, it is interesting to create new images in which the correlation between the images is vanished and the mutual information is minimized. To reach such purpose, we have developed a new approach for seasonal vegetation analysis based on a new statistical multi-variate method called independent component analysis (ICA).