  Spatial correlations between different ground objects are an important
feature of mining land cover research. Graph Convolutional Networks (GCNs) can
effectively capture such spatial feature representations and have demonstrated
promising results in performing hyperspectral imagery (HSI) classification
tasks of complex land. However, the existing GCN-based HSI classification
methods are prone to interference from redundant information when extracting
complex features. To classify complex scenes more effectively, this study
proposes a novel spatial-spectral reliable contrastive graph convolutional
classification framework named S2RC-GCN. Specifically, we fused the spectral
and spatial features extracted by the 1D- and 2D-encoder, and the 2D-encoder
includes an attention model to automatically extract important information. We
then leveraged the fused high-level features to construct graphs and fed the
resulting graphs into the GCNs to determine more effective graph
representations. Furthermore, a novel reliable contrastive graph convolution
was proposed for reliable contrastive learning to learn and fuse robust
features. Finally, to test the performance of the model on complex object
classification, we used imagery taken by Gaofen-5 in the Jiang Xia area to
construct complex land cover datasets. The test results show that compared with
other models, our model achieved the best results and effectively improved the
classification performance of complex remote sensing imagery.
