S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images

arXiv:2404.00964v1 Announce Type: new
Abstract: 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 …

abstract arxiv classification correlations cs.cv feature graph images mining network networks objects research results spatial tasks type