Integrating fine scale information in super-resolution land-cover mapping

aboucher - Posted on 12 May 2009

TitleIntegrating fine scale information in super-resolution land-cover mapping
Publication TypeJournal Article
Year of Publication2007
AuthorsBoucher, A, Kyriakidis PC
Date PublishedAUG
AbstractSuper-resolution or sub-pixel class mapping is the task of providing fine spatial resolution maps of, for example, landcover classes, from satellite sensor measurements obtained at a coarser spatial resolution. Often, the only information available consists of coarse class fraction data, typically obtained through spectral unmixing.. This paper shows how to integrate, in addition to such coarse fractions, class labels at a set of fine pixels obtained independent of the satellite sensor measurements. The integration of such fine spatial resolution information is achieved within the Indicator Kriging formalism in either a prediction or simulation mode. The spatial dissimilarity or texture of class labels at the fine (target) resolution is quantified in a non-parametric way from an analog scene using a set of experimental indicator semivariogram maps. The output of the proposed procedure consists of maps of probabilities of class occurrence, or of a series of simulated class maps characterizing the inherent spatial uncertainty in the super-resolution mapping process.