  To read the final version please go to IEEE TGRS on IEEE Xplore.
Convolutional neural networks (CNNs) have been attracting increasing attention
in hyperspectral (HS) image classification, owing to their ability to capture
spatial-spectral feature representations. Nevertheless, their ability in
modeling relations between samples remains limited. Beyond the limitations of
grid sampling, graph convolutional networks (GCNs) have been recently proposed
and successfully applied in irregular (or non-grid) data representation and
analysis. In this paper, we thoroughly investigate CNNs and GCNs (qualitatively
and quantitatively) in terms of HS image classification. Due to the
construction of the adjacency matrix on all the data, traditional GCNs usually
suffer from a huge computational cost, particularly in large-scale remote
sensing (RS) problems. To this end, we develop a new mini-batch GCN (called
miniGCN hereinafter) which allows to train large-scale GCNs in a mini-batch
fashion. More significantly, our miniGCN is capable of inferring out-of-sample
data without re-training networks and improving classification performance.
Furthermore, as CNNs and GCNs can extract different types of HS features, an
intuitive solution to break the performance bottleneck of a single model is to
fuse them. Since miniGCNs can perform batch-wise network training (enabling the
combination of CNNs and GCNs) we explore three fusion strategies: additive
fusion, element-wise multiplicative fusion, and concatenation fusion to measure
the obtained performance gain. Extensive experiments, conducted on three HS
datasets, demonstrate the advantages of miniGCNs over GCNs and the superiority
of the tested fusion strategies with regards to the single CNN or GCN models.
The codes of this work will be available at
https://github.com/danfenghong/IEEE_TGRS_GCN for the sake of reproducibility.
