  In hyperspectral image (HSI) classification, spatial context has demonstrated
its significance in achieving promising performance. However, conventional
spatial context-based methods simply assume that spatially neighboring pixels
should correspond to the same land-cover class, so they often fail to correctly
discover the contextual relations among pixels in complex situations, and thus
leading to imperfect classification results on some irregular or inhomogeneous
regions such as class boundaries. To address this deficiency, we develop a new
HSI classification method based on the recently proposed Graph Convolutional
Network (GCN), as it can flexibly encode the relations among arbitrarily
structured non-Euclidean data. Different from traditional GCN, there are two
novel strategies adopted by our method to further exploit the contextual
relations for accurate HSI classification. First, since the receptive field of
traditional GCN is often limited to fairly small neighborhood, we proposed to
capture long range contextual relations in HSI by performing successive graph
convolutions on a learned region-induced graph which is transformed from the
original 2D image grids. Second, we refine the graph edge weight and the
connective relationships among image regions by learning the improved adjacency
matrix and the 'edge filter', so that the graph can be gradually refined to
adapt to the representations generated by each graph convolutional layer. Such
updated graph will in turn result in accurate region representations, and vice
versa. The experiments carried out on three real-world benchmark datasets
demonstrate that the proposed method yields significant improvement in the
classification performance when compared with some state-of-the-art approaches.
