1 citations · 2 across the 4 of their papers we have counts for
6 papers
Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
Jingxin Wang, Renxiang Guan, Kainan Gao +4
Hyperspectral image (HSI) clustering is a challenging task due to its high complexity. Despite subspace clustering shows impressive performance for HSI, traditional methods tend to…
S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images
Renxiang Guan, Zihao Li, Chujia Song +3
Spatial correlations between different ground objects are an important feature of mining land cover research. Graph Convolutional Networks (GCNs) can effectively capture such spati…
Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
Xianju Li, Renxiang Guan, Zihao Li +2
High-dimensional and complex spectral structures make clustering of hy-perspectral images (HSI) a challenging task. Subspace clustering has been shown to be an effective approach f…
Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering
Renxiang Guan, Zihao Li, Xianju Li +1
Hyperspectral image (HSI) clustering is gaining considerable attention owing to recent methods that overcome the inefficiency and misleading results from the absence of supervised…
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
Renxiang Guan, Zihao Li, Xianju Li +2
High-dimensional and complex spectral structures make the clustering of hyperspectral images (HSI) a challenging task. Subspace clustering is an effective approach for addressing t…
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
Zhenglai Li, Chang Tang, Xinwang Liu +3
Fully supervised change detection methods have achieved significant advancements in performance, yet they depend severely on acquiring costly pixel-level labels. Considering that t…