  Hyperspectral image classification (HIC) is an important but challenging
task, and a problem that limits the algorithmic development in this field is
that the ground truths of hyperspectral images (HSIs) are extremely hard to
obtain. Recently a handful of HIC methods are developed based on the graph
convolution networks (GCNs), which effectively relieves the scarcity of labeled
data for deep learning based HIC methods. To further lift the classification
performance, in this work we propose a graph convolution network (GCN) based
framework for HSI classification that uses two clustering operations to better
exploit multi-hop node correlations and also effectively reduce graph size. In
particular, we first cluster the pixels with similar spectral features into a
superpixel and build the graph based on the superpixels of the input HSI. Then
instead of performing convolution over this superpixel graph, we further
partition it into several sub-graphs by pruning the edges with weak weights, so
as to strengthen the correlations of nodes with high similarity. This second
round of clustering also further reduces the graph size, thus reducing the
computation burden of graph convolution. Experimental results on three widely
used benchmark datasets well prove the effectiveness of our proposed framework.
