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Found 5 papers, 5 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.
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image
The framework consists of a band attention module (BAM), which aims to explicitly model the nonlinear inter-dependencies between spectral bands, and a reconstruction network (RecNet), which is used to restore the original HSI cube from the learned informative bands, resulting in a flexible architecture.
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery
(1) Unlike the traditional PCA method based on a whole image, SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections.
