Advanced deep learning for hyperspectral data classification

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