# Remote sensing framework for geological mapping via stacked autoencoders and clustering

arXiv: https://arxiv.org/abs/2404.02180 · HTML: https://mlsanity.com/paper/2404.02180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 115 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [A review of machine learning in processing remote sensing data for mineral exploration](https://mlsanity.com/paper/2103.07678/) (2021)
- [Local Deep-Feature Alignment for Unsupervised Dimension Reduction](https://mlsanity.com/paper/1904.09747/) (2019)
- [Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders](https://mlsanity.com/paper/1907.08870/) (2019)
- [Machine learning based hyperspectral image analysis: A survey](https://mlsanity.com/paper/1802.08701/) (2018)
- [Deep learning for lithological classification of carbonate rock micro-CT images](https://mlsanity.com/paper/2007.15693/) (2020)
- [Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared](https://mlsanity.com/paper/2106.13315/) (2021)
- [Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing](https://mlsanity.com/paper/2301.10876/) (2023)

## Abstract (arXiv)

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. We present 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 (PCA) and canonical autoencoders. Our results reveal that the framework produces accurate and interpretable geological maps, efficiently discriminating rock units. The results reveal that the combination of stacked autoencoders with Sentinel-2 data yields the best performance accuracy when compared to other combinations. We find that stacked autoencoders enable better extraction of complex and hierarchical representations of the input data when compared to canonical autoencoders and PCA. We also find that the generated maps align with prior geological knowledge of the study area while providing novel insights into geological structures.