Remote sensing framework for geological mapping via stacked autoencoders
  and clustering
Sandeep Nagar Ehsan Farahbakhsh Joseph Awange Rohitash Chandra
Supervised learning methods for geological mapping via remote sensing face
limitations due to the scarcity of accurately labelled training data. In
contrast, unsupervised learning methods, such as dimensionality reduction and
clustering have the ability to uncover patterns and structures in remote
sensing data without relying on predefined labels. 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 have the ability to model nonlinear relationship in data. Stacked
autoencoders feature multiple interconnected layers to capture hierarchical
data representations that can be useful for remote sensing data. In this study,
we present an unsupervised machine learning framework for processing remote
sensing data by utilizing stacked autoencoders for dimensionality reduction and
k-means clustering for mapping geological units. We use the Landsat-8, ASTER,
and Sentinel-2 datasets of the Mutawintji region in Western New South Wales,
Australia to evaluate the framework for geological mapping. We also provide a
comparison of 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 stacked autoencoders provide better accuracy when compared to the
counterparts. We also find that the generated maps align with prior geological
knowledge of the study area while providing novel insights into geological
structures.