  Convolutional Neural Network (CNN) has demonstrated impressive ability to
represent hyperspectral images and to achieve promising results in
hyperspectral image classification. However, traditional CNN models can only
operate convolution on regular square image regions with fixed size and
weights, so they cannot universally adapt to the distinct local regions with
various object distributions and geometric appearances. Therefore, their
classification performances are still to be improved, especially in class
boundaries. To alleviate this shortcoming, we consider employing the recently
proposed Graph Convolutional Network (GCN) for hyperspectral image
classification, as it can conduct the convolution on arbitrarily structured
non-Euclidean data and is applicable to the irregular image regions represented
by graph topological information. Different from the commonly used GCN models
which work on a fixed graph, we enable the graph to be dynamically updated
along with the graph convolution process, so that these two steps can be
benefited from each other to gradually produce the discriminative embedded
features as well as a refined graph. Moreover, to comprehensively deploy the
multi-scale information inherited by hyperspectral images, we establish
multiple input graphs with different neighborhood scales to extensively exploit
the diversified spectral-spatial correlations at multiple scales. Therefore,
our method is termed 'Multi-scale Dynamic Graph Convolutional Network' (MDGCN).
The experimental results on three typical benchmark datasets firmly demonstrate
the superiority of the proposed MDGCN to other state-of-the-art methods in both
qualitative and quantitative aspects.
