Spatio-temporal deconvolution of NDVI image sequences using independent component analysis

Independent component analysis (ICA) provides a powerful new method to spatially and temporally deconvolve image sequences into components that capture variability arising from independent physical sources. To do this, ICA uses information contained in higher order cross-moments of multivariate data. We use remotely sensed time series of the normalized difference vegetation index to illustrate the utility of this technique.