Geostatistical solutions for super-resolution land cover mapping
Journal
IEEE Transactions on Geoscience and Remote Sensing
Date Issued
January 2008
Abstract
Super-resolution land cover mapping aims at producing
fine spatial resolution maps of land cover classes from
a set of coarse-resolution class fractions derived from satellite
information via, for example, spectral unmixing procedures. Based
on a prior model of spatial structure or texture that encodes the
expected patterns of classes at the fine (target) resolution, this
paper presents a sequential simulation framework for generating
alternative super-resolution maps of class labels that are consistent
with the coarse class fractions. Two modes of encapsulating the
prior structural information are investigated—one uses a set of
indicator variogram models, and the other uses training images. A
case study illustrates that both approaches lead to super-resolution
class maps that exhibit a variety of spatial patterns ranging from
simple to complex. Using four different examples, it is demonstrated
that the structural model controls the patterns seen on the
super-resolution maps, even for cases where the coarse fraction
data are highly constraining.
fine spatial resolution maps of land cover classes from
a set of coarse-resolution class fractions derived from satellite
information via, for example, spectral unmixing procedures. Based
on a prior model of spatial structure or texture that encodes the
expected patterns of classes at the fine (target) resolution, this
paper presents a sequential simulation framework for generating
alternative super-resolution maps of class labels that are consistent
with the coarse class fractions. Two modes of encapsulating the
prior structural information are investigated—one uses a set of
indicator variogram models, and the other uses training images. A
case study illustrates that both approaches lead to super-resolution
class maps that exhibit a variety of spatial patterns ranging from
simple to complex. Using four different examples, it is demonstrated
that the structural model controls the patterns seen on the
super-resolution maps, even for cases where the coarse fraction
data are highly constraining.