Sampling strategies for unsupervised classification of multitemporal high resolution optical images over very large areas

Efficient unsupervised production of large-area land cover maps with the volumes of data to be generated by the forthcoming Earth observation missions is challenging in terms of computation costs and data variability. As a solution, introduction of non-spectral knowledge for data reduction and selection is proposed here. Analysis of intra-strata variability and inter-strata correlation for different stratified sampling approaches is presented, and valuable variables for both stratification and classification are identified.