  It is well known that hyperspectral images (HSI) contain rich
spatial-spectral contextual information, and how to effectively combine both
spectral and spatial information using DNN for HSI classification has become a
new research hotspot. Compared with CNN with square kernels, GCN have exhibited
exciting potential to model spatial contextual structure and conduct flexible
convolution on arbitrarily irregular image regions. However, current GCN only
using first-order spectral-spatial signatures can result in boundary blurring
and isolated misclassification. To address these, we first designed the
graph-based second-order pooling (GSOP) operation to obtain contextual nodes
information in non-Euclidean space for GCN. Further, we proposed a novel
multi-stream hybridized convolutional network (MSHCNet) with combination of
first and second order statistics in Euclidean/non-Euclidean spaces to learn
and fuse multi-view complementary information to segment HSIs. Specifically,
our MSHCNet adopted four parallel streams, which contained G-stream, utilizing
the irregular correlation between adjacent land covers in terms of first-order
graph in non-Euclidean space; C-stream, adopting convolution operator to learn
regular spatial-spectral features in Euclidean space; N-stream, combining first
and second order features to learn representative and discriminative regular
spatial-spectral features of Euclidean space; S-stream, using GSOP to capture
boundary correlations and obtain graph representations from all nodes in graphs
of non-Euclidean space. Besides, these feature representations learned from
four different streams were fused to integrate the multi-view complementary
information for HSI classification. Finally, we evaluated our proposed MSHCNet
on three hyperspectral datasets, and experimental results demonstrated that our
method significantly outperformed state-of-the-art eight methods.
