  Hyperspectral images (HSIs) can distinguish materials with high number of
spectral bands, which is widely adopted in remote sensing applications and
benefits in high accuracy land cover classifications. However, HSIs processing
are tangled with the problem of high dimensionality and limited amount of
labelled data. To address these challenges, this paper proposes a deep learning
architecture using three dimensional convolutional neural networks with
spectral partitioning to perform effective feature extraction. We conduct
experiments using Indian Pines and Salinas scenes acquired by NASA Airborne
Visible/Infra-Red Imaging Spectrometer. In comparison to prior results, our
architecture shows competitive performance for classification results over
current methods.
