3 papers
cs.CV2025
Joint Superpixel and Self-Representation Learning for Scalable Hyperspectral Image Clustering
Xianlu Li, Nicolas Nadisic, Shaoguang Huang +1
Subspace clustering is a powerful unsupervised approach for hyperspectral image (HSI) analysis, but its high computational and memory costs limit scalability. Superpixel segmentati…
cs.CV2025
Scalable Context-Preserving Model-Aware Deep Clustering for Hyperspectral Images
Xianlu Li, Nicolas Nadisic, Shaoguang Huang +2
Subspace clustering has become widely adopted for the unsupervised analysis of hyperspectral images (HSIs). Recent model-aware deep subspace clustering methods often use a two-stag…
cs.CV2024
Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images
Xianlu Li, Nicolas Nadisic, Shaoguang Huang +1
Deep subspace clustering methods are now prominent in clustering, typically using fully connected networks and a self-representation loss function. However, these methods often str…