# Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2312.06068 · HTML: https://mlsanity.com/paper/2312.06068/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review](https://mlsanity.com/paper/2001.07307/) (2020)
- [Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images](https://mlsanity.com/paper/2106.10605/) (2021)
- [Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods](https://mlsanity.com/paper/1310.5107/) (2013)
- [On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization](https://mlsanity.com/paper/1610.07126/) (2016)
- [Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization](https://mlsanity.com/paper/1709.01212/) (2017)
- [Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image](https://mlsanity.com/paper/2004.10476/) (2020)
- [Convex Sparse Spectral Clustering: Single-view to Multi-view](https://mlsanity.com/paper/1511.06860/) (2015)
- [Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction](https://mlsanity.com/paper/2009.10003/) (2020)
- [Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification](https://mlsanity.com/paper/2301.03335/) (2023)
- [Adaptive Attribute and Structure Subspace Clustering Network](https://mlsanity.com/paper/2109.13742/) (2021)
- [Understand and Improve Contrastive Learning Methods for Visual Representation: A Review](https://mlsanity.com/paper/2106.03259/) (2021)

## Built on by (most cited first, 11 of 11)

- [LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network](https://mlsanity.com/paper/2404.01614/) (2024)
- [Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.09630/) (2023)
- [Part-Attention Based Model Make Occluded Person Re-Identification Stronger](https://mlsanity.com/paper/2404.03443/) (2024)
- [Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering](https://mlsanity.com/paper/2404.05211/) (2024)
- [One-Step Late Fusion Multi-view Clustering with Compressed Subspace](https://mlsanity.com/paper/2401.01558/) (2024)
- [S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images](https://mlsanity.com/paper/2404.00964/) (2024)
- [GLADformer: A Mixed Perspective for Graph-level Anomaly Detection](https://mlsanity.com/paper/2406.00734/) (2024)
- [CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution](https://mlsanity.com/paper/2408.00794/) (2024)
- [Case-based reasoning approach for diagnostic screening of children with developmental delays](https://mlsanity.com/paper/2408.02073/) (2024)
- [A Local Perspective-based Model for Overlapping Community Detection](https://mlsanity.com/paper/2503.21558/) (2025)
- [Subspace Clustering on Incomplete Data with Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2602.00262/) (2026)

## Abstract (arXiv)

High-dimensional and complex spectral structures make the clustering of hyperspectral images (HSI) a challenging task. Subspace clustering is an effective approach for addressing this problem. However, current subspace clustering algorithms are primarily designed for a single view and do not fully exploit the spatial or textural feature information in HSI. In this study, contrastive multi-view subspace clustering of HSI was proposed based on graph convolutional networks. Pixel neighbor textural and spatial-spectral information were sent to construct two graph convolutional subspaces to learn their affinity matrices. To maximize the interaction between different views, a contrastive learning algorithm was introduced to promote the consistency of positive samples and assist the model in extracting robust features. An attention-based fusion module was used to adaptively integrate these affinity matrices, constructing a more discriminative affinity matrix. The model was evaluated using four popular HSI datasets: Indian Pines, Pavia University, Houston, and Xu Zhou. It achieved overall accuracies of 97.61%, 96.69%, 87.21%, and 97.65%, respectively, and significantly outperformed state-of-the-art clustering methods. In conclusion, the proposed model effectively improves the clustering accuracy of HSI.