Integrating fine scale information in super-resolution land cover mapping
Journal
Photogrammetric Engineering & Remote Sensing
Date Issued
August 2007
Author(s)
DOI
10.14358/PERS.73.8.913
Abstract
Super-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.
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.