Advanced deep learning for hyperspectral data classification

Wijayanti Nurul Khotimah

Research output: ThesisDoctoral Thesis

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Abstract

This thesis explores advanced deep-learning techniques for classifying hyperspectral data. Wescrutinize the background and context of hyperspectral data classification. Then, we recommend fourdeep learning architectures for this purpose. First, we introduce a two-stream spectral-spatial residualnetwork suitable for hyperspectral image (HSI) classification. Second, we propose a spatiospectralmasked autoencoder and a hybrid episode learning strategy to classify scarce HSI datasets. Third,we introduce a spectral convolution and channel attention network to capture short- and long-rangedependencies between spectral bands. Finally, we propose a transformer-based network thatincorporates a spectral-to-token module and a multiscale conformer encoder module
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Supervisors/Advisors
  • Bennamoun, Mohammed, Supervisor
  • Boussaid, Farid, Supervisor
  • Edwards, Dave, Supervisor
  • Sohel, Ferdous, Supervisor
Award date9 May 2024
DOIs
Publication statusUnpublished - 2023

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