  In this paper, we propose an unified hyperspectral image classification
method which takes three-dimensional hyperspectral data cube as an input and
produces a classification map. In the proposed method, a deep neural network
which uses spectral and spatial information together with residual connections,
and pixel affinity network based segmentation-aware superpixels are used
together. In the architecture, segmentation-aware superpixels run on the
initial classification map of deep residual network, and apply majority voting
on obtained results. Experimental results show that our propoped method yields
state-of-the-art results in two benchmark datasets. Moreover, we also show that
the segmentation-aware superpixels have great contribution to the success of
hyperspectral image classification methods in cases where training data is
insufficient.
