Abstract
As more and more remotely sensed data becomes available it is becoming increasingly
harder to analyse it with the more traditional labour intensive, manual
methods. The commonly used techniques, that involve expert evaluation, are
widely acknowledged as providing inconsistent results, at best. We need more
general techniques that can adapt to a given situation and that incorporate the
strengths of the traditional methods, human operators and new technologies.
The difficulty in interpreting remotely sensed data is that often only a small
amount of data is available for classification. It can be noisy, incomplete or contain
irrelevant information.
Given that the training data may be limited we demonstrate a variety of techniques
for highlighting information in the available data and how to select the most
relevant information for a given classification task. We show that more consistent
results between the training data and an entire image can be obtained, and how
misclassification errors can be reduced. Specifically, a new technique for attribute
selection in neural networks is demonstrated.
Machine learning techniques, in particular, provide us with a means of automating
classification using training data from a variety of data sources, including remotely
sensed data and expert knowledge.
A classification framework is presented in this thesis that can be used with any
classifier and any available data. While this was developed in the context of
vegetation mapping from remotely sensed data using machine learning classifiers,
it is a general technique that can be applied to any domain. The emphasis of
the applicability for this framework being domains that have inadequate training
data available.