Super-resolution mapping using the Hopfield neural network with supplementary data

Abstract

New approaches for using supplementary data such as panchromatic and fused imagery and Light Detection And Ranging (LiDAR) elevation data to increase the accuracy and spatial resolution of the thematic map were developed in this thesis.  Information from the fused and panchromatic imagery was incorporated into the Hopfield neural network (HNN) model based forward and inverse models in form of reflectance functions.  For the fused image, the forward and inverse models were formulated based on a linear mixture model and local end-member values.  The reflectance function for the panchromatic image was derived locally based on the spectral and spatial convolutions. Visual and statistical analyses demonstrated that the use of fused and panchromatic imagery can increase accuracy of the sub-pixel image.  The HNN super-resolution mapping using LiDAR elevation data is based on an optimisation process with a probability maximisation for the building class as a goal together with the goal functions and constraints of the traditional super-resolution mapping. The results showed a considerable increase in all accuracy statistics of the new technique, particularly for building objects.

Adopting the HNN model and forward model mechanism, three approaches for super-resolving of fine sub-pixel multispectral (MS) image from the coarse MS imagery were developed based on the HNN super-resolution mapping technique with the forward model and semivariogram matching.  The first approach can be applied to predict the sub-pixel image based on the super-resolution of the mixed pixels. The second approach can be used to create the sub-pixel MS image with spectral features of the coarse resolution image and spatial variation at sub-pixel resolution.  The aim of the third approach is to generate a smoothed sub-pixel image by maximising the spatial dependence between the sub-pixels on a semivariance value of zero at lag h =1.

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