# Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification Source: [Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:20:40.032Z Authors: Yuqing Zhang, Qi Han, Ligeng Wang, Kai Cheng, Bo Wang, Kun Zhan Published: 2025-03-19T22:55:52.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()