  Supervised machine learning methods for geological mapping via remote sensing
face limitations due to the scarcity of accurately labelled training data that
can be addressed by unsupervised learning, such as dimensionality reduction and
clustering. Dimensionality reduction methods have the potential to play a
crucial role in improving the accuracy of geological maps. Although
conventional dimensionality reduction methods may struggle with nonlinear data,
unsupervised deep learning models such as autoencoders can model non-linear
relationships. Stacked autoencoders feature multiple interconnected layers to
capture hierarchical data representations useful for remote sensing data. This
study presents an unsupervised machine learning-based framework for processing
remote sensing data using stacked autoencoders for dimensionality reduction and
k-means clustering for mapping geological units. We use Landsat 8, ASTER, and
Sentinel-2 datasets to evaluate the framework for geological mapping of the
Mutawintji region in Western New South Wales, Australia. We also compare
stacked autoencoders with principal component analysis and canonical
autoencoders. Our results reveal that the framework produces accurate and
interpretable geological maps, efficiently discriminating rock units. We find
that the accuracy of stacked autoencoders ranges from 86.6 % to 90 %, depending
on the remote sensing data type, which is superior to their counterparts. We
also find that the generated maps align with prior geological knowledge of the
study area while providing novel insights into geological structures.
