  Convolutional neural networks (CNN) have made significant advances in
hyperspectral image (HSI) classification. However, standard convolutional
kernel neglects the intrinsic connections between data points, resulting in
poor region delineation and small spurious predictions. Furthermore, HSIs have
a unique continuous data distribution along the high dimensional spectrum
domain - much remains to be addressed in characterizing the spectral contexts
considering the prohibitively high dimensionality and improving reasoning
capability in light of the limited amount of labelled data. This paper presents
a novel architecture which explicitly addresses these two issues. Specifically,
we design an architecture to encode the multiple spectral contextual
information in the form of spectral pyramid of multiple embedding spaces. In
each spectral embedding space, we propose graph attention mechanism to
explicitly perform interpretable reasoning in the spatial domain based on the
connection in spectral feature space. Experiments on three HSI datasets
demonstrate that the proposed architecture can significantly improve the
classification accuracy compared with the existing methods.
