Development of deep learning-based classification and unsupervised clustering methods for mineral mapping using remotely sensed hyperspectral data
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Date
2024-12
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University of New Brunswick
Abstract
Hyperspectral remotely sensed imagery is a powerful tool for mineral mapping. It captures detailed spectral information across hundreds of contiguous and narrow spectral bands to enable precise identification of various geological materials. Conventional methods mainly use shallow spectral absorption features to discriminate minerals and cannot extract their important spectral information. However, traditional methods face significant challenges in effectively handling hyperspectral data's high dimensionality, nonlinear spectral features, and low signal-to-noise ratio (SNR). These challenges limit the accuracy of traditional machine-learning algorithms in mapping the spectral variations of minerals.
This PhD research addresses these limitations through a comprehensive literature review and the development of new methods. It has resulted in two published journal papers and one submitted journal paper, presented across three chapters of this dissertation.
The third chapter of this dissertation (published review paper) provides an updated systematic overview of hyperspectral missions, diagnostic minerals' spectral properties, and various geologic information extraction techniques, including preprocessing, dimension reduction, endmember retrieval, and important image classification methods from spaceborne/airborne HSI. It evaluates the advantages and limitations of the existing conventional methods of processing HSIs with the aim of geological mapping.
The fourth chapter (published paper) aims to improve the accuracy of spectral-spatial deep learning extractors in classifying HSI datasets. While traditional deep learning methods such as fully connected neural networks (FCNN), convolutional neural networks (CNNs), and hybrid CNNs like mixed convolutions and covariance pooling (MCNN-CP) algorithms have shown promise, they face limitations in robustness and accuracy. This proposes an integrated 1D, 2D, and 3D CNN architecture to enhance the capability of spectral-spatial extractors, significantly improving classification accuracy and resilience.
The fifth chapter (submitted) explores deep learning-based clustering methods for unsupervised mineral mapping, which are valuable in remote areas where ground truth data is scarce. These methods leverage HSIs' high-dimensional and redundant spectral features, using advanced clustering techniques to generate accurate mineral maps without requiring extensive labelled data. This research proposes a hybrid 3D-2D convolutional autoencoder to capture HSI's spatial and spectral diversity.
The anticipated outcomes include enhanced accuracy and computational efficiency, ultimately improving the utility of HSI for geological studies and resource exploration.
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NATURAL SCIENCES::Earth sciences::Endogenous earth sciences::Mineralogy