# Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2503.15731 · HTML: https://mlsanity.com/paper/2503.15731/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network](https://mlsanity.com/paper/1909.11953/) (2019)
- [Hyperspectral Image Classification in the Presence of Noisy Labels](https://mlsanity.com/paper/1809.04212/) (2018)
- [Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/1903.06548/) (2019)
- [Hyperspectral Image Classification With Contrastive Graph Convolutional Network](https://mlsanity.com/paper/2205.11237/) (2022)
- [InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification](https://mlsanity.com/paper/2404.11003/) (2024)
- [Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network](https://mlsanity.com/paper/2307.13938/) (2023)
- [Entropy Neural Estimation for Graph Contrastive Learning](https://mlsanity.com/paper/2307.13944/) (2023)

## Built on by (most cited first, 2 of 2)

- [Graph Your Own Prompt](https://mlsanity.com/paper/2509.23373/) (2025)
- [High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification](https://mlsanity.com/paper/2604.26279/) (2026)

## Abstract (arXiv)

Most existing graph-based semi-supervised hyperspectral image classification methods rely on superpixel partitioning techniques. However, they suffer from misclassification of certain pixels due to inaccuracies in superpixel boundaries, ie, the initial inaccuracies in superpixel partitioning limit overall classification performance. In this paper, we propose a novel graph-weighted contrastive learning approach that avoids the use of superpixel partitioning and directly employs neural networks to learn hyperspectral image representation. Furthermore, while many approaches require all graph nodes to be available during training, our approach supports mini-batch training by processing only a subset of nodes at a time, reducing computational complexity and improving generalization to unseen nodes. Experimental results on three widely-used datasets demonstrate the effectiveness of the proposed approach compared to baselines relying on superpixel partitioning.