Browsing by Author "Peyghambari, Sima"

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    Development of deep learning-based classification and unsupervised clustering methods for mineral mapping using remotely sensed hyperspectral data
    (University of New Brunswick, 2024-12) Peyghambari, Sima; Zhang, Yun
    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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    Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review
    (Society of Photo-optical Instrumentation Engineers, 2021) Peyghambari, Sima; Zhang, Yun
    Hyperspectral imaging has been used in a variety of geological applications since its advent in the 1970s. In the last few decades, different techniques have been developed by geologists to analyze hyperspectral data in order to quantitatively extract geological information from the high-spectral-resolution remote sensing images. We attempt to review and update various steps of the techniques used in geological information extraction, such as lithological and mineralogical mapping, ore exploration, and environmental geology. The steps include atmospheric correction, dimensionality processing, endmember extraction, and image classification. It is identified that per-pixel and subpixel image classifiers can generate accurate alteration mineral maps. Producing geological maps of different surface materials including minerals and rocks is one of the most important geological applications. The hyperspectral images classification methods demonstrate the potential for being used as a main tool in the mining industry and environmental geology. To exemplify the potential, we also include a few case studies of different geological applications.