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.

提出了一种名为 S2RC-GCN 的新型空间光谱可靠对比图卷积分类框架，通过融合一维和二维编码器提取的光谱和空间特征，构建图形并将其输入到图卷积网络中以确定更有效的图形表示，进一步提出了一种可靠的对比图卷积方法以学习和融合鲁棒特征，测试结果表明该模型在复杂对象分类方面取得了最佳效果，有效提高了复杂遥感图像的分类性能。