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Found 3 papers, 3 papers with code
Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool.
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering
This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning.
Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image
In this paper, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering.
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