  In this thesis, we derive a catalogue of robust structural parameters for the
components of a large sample of nearby GAMA galaxies while at the same time
contributing to the advancement of image analysis, surface brightness fitting
and post-processing routines for quality assurance in the context of automated
large-scale bulge-disk decomposition studies. The sample consists of 13096
galaxies at redshifts z < 0.08 with imaging data from the Kilo-Degree Survey
and the VISTA Kilo-Degree INfrared Galaxy survey spanning the optical and
near-infrared. We fit three models to the surface brightness distribution of
each galaxy in each band: a single S\'ersic model, a S\'ersic plus exponential
and a point source plus exponential. The fitting is performed with a fully
automated Markov-chain Monte Carlo (MCMC) analysis using the Bayesian
two-dimensional profile fitting code ProFit. All preparatory work is carried
out using the image analysis package ProFound. After fitting the galaxies, we
perform model selection and flag galaxies for which none of our models are
appropriate, mainly mergers and irregular galaxies. The fit quality is assessed
by visual inspections, comparison to previous works, comparison of independent
fits of galaxies in the overlap regions between KiDS tiles and bespoke
simulations. The latter two are also used for a detailed investigation of
systematic error sources. We find that our fit results are robust across
various galaxy types and image qualities with minimal biases. Errors given by
the MCMC underestimate the true errors typically by factors 2-3. Automated
model selection criteria are accurate to > 90 % as calibrated by visual
inspection of a subsample of galaxies. We also present g-r component colours
and the corresponding colour-magnitude diagram, consistent with previous works
despite our increased fit flexibility. All results are integrated into the GAMA
database.
