Superresolution mapping using a hopfield neural network with fused images

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Description/Abstract

Superresolution mapping is a set of techniques to increase
the spatial resolution of a land cover map obtained by softclassification
methods. In addition to the information from the land
cover proportion images, supplementary information at the subpixel
level can be used to produce more detailed and accurate land
cover maps. The proposed method in this research aims to use
fused imagery as an additional source of information for superresolution
mapping using the Hopfield neural network (HNN). Forward
and inverse models were incorporated in the HNN to support
a new reflectance constraint added to the energy function.
The value of the function was calculated based on a linear mixture
model. In addition, a new model was used to calculate the local endmember
spectra for the reflectance constraint. A set of simulated
images was used to test the new technique. The results suggest that
fine spatial resolution fused imagery can be used as supplementary
data for superresolution mapping from a coarser spatial resolution
land cover proportion imagery.