  Graphs naturally lend themselves to model the complexities of Hyperspectral
Image (HSI) data as well as to serve as semi-supervised classifiers by
propagating given labels among nearest neighbours. In this work, we present a
novel framework for the classification of HSI data in light of a very limited
amount of labelled data, inspired by multi-view graph learning and graph signal
processing. Given an a priori superpixel-segmented hyperspectral image, we seek
a robust and efficient graph construction and label propagation method to
conduct semi-supervised learning (SSL). Since the graph is paramount to the
success of the subsequent classification task, particularly in light of the
intrinsic complexity of HSI data, we consider the problem of finding the
optimal graph to model such data. Our contribution is two-fold: firstly, we
propose a multi-stage edge-efficient semi-supervised graph learning framework
for HSI data which exploits given label information through pseudo-label
features embedded in the graph construction. Secondly, we examine and enhance
the contribution of multiple superpixel features embedded in the graph on the
basis of pseudo-labels in an extension of the previous framework, which is less
reliant on excessive parameter tuning. Ultimately, we demonstrate the
superiority of our approaches in comparison with state-of-the-art methods
through extensive numerical experiments.
