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Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
Multi-level graph subspace contrastive learning: multi-level contrastive learning was conducted to obtain local-global joint graph representations, to improve the consistency of the positive samples between views, and to obtain more robust graph embeddings.
S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images
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.
Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
In conclusion, the proposed model can effectively improve the clustering ac-curacy of HSI.
Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering
The pixel-level contrastive learning method can effectively improve the ability of the model to capture fine features of HSI but requires a large time overhead.
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
In this study, contrastive multi-view subspace clustering of HSI was proposed based on graph convolutional networks.
