Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
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Abstract (arXiv)
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 ignore the global-local interaction in HSI data. In this study, we proposed a multi-level graph subspace contrastive learning (MLGSC) for HSI clustering. The model is divided into the following main parts. Graph convolution subspace construction: utilizing spectral and texture feautures to construct two graph convolution views. Local-global graph representation: local graph representations were obtained by step-by-step convolutions and a more representative global graph representation was obtained using an attention-based pooling strategy. 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. Specifically, graph-level contrastive learning is used to better learn global representations of HSI data. Node-level intra-view and inter-view contrastive learning is designed to learn joint representations of local regions of HSI. The proposed model is evaluated on four popular HSI datasets: Indian Pines, Pavia University, Houston, and Xu Zhou. The overall accuracies are 97.75%, 99.96%, 92.28%, and 95.73%, which significantly outperforms the current state-of-the-art clustering methods.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 8 Apr 2024.
Builds on
20 of 56 references.
References · 20
- 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
- 2020Deep Fusion Clustering Networkcited by 71
- 2022Hard Sample Aware Network for Contrastive Deep Graph Clusteringcited by 61
- 2022Simple Contrastive Graph Clusteringcited by 56
- 2023Dink-Net: Neural Clustering on Large Graphscited by 38
- 2023Towards High-quality HDR Deghosting with Conditional Diffusion Modelscited by 33
- 2021Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Imagescited by 31
- 2017Multi-view Low-rank Sparse Subspace Clusteringcited by 27
- 2021GaTector: A Unified Framework for Gaze Object Predictioncited by 18
- 2020Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Imagecited by 16
- 2023A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspectivecited by 14
- 2023Deep Multi-View Subspace Clustering with Anchor Graphcited by 13
8 more
- 2023Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networkscited by 11
- 2023Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clusteringcited by 11
- 2023Multi-class Support Vector Machine with Maximizing Minimum Margincited by 6
- 2024Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clusteringcited by 6
- 2023MCoCo: Multi-level Consistency Collaborative Multi-view Clusteringcited by 5
- 2023Multi-view Semantic Consistency based Information Bottleneck for Clusteringcited by 4
- 2023Mixture of personality improved Spiking actor network for efficient multi-agent cooperationcited by 4
- 2024Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networkscited by 1
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.