不依赖超像素分割，用图加权对比学习提升高光谱图像分类精度。
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification
- 直接用神经网络学习高光谱特征，避开超像素分割误差
- 支持小批量训练，可处理部分节点，降低计算开销
- 在三个数据集上优于传统超像素方法，适合资源受限场景
现有基于图的半监督高光谱图像分类方法多依赖超像素分割技术，但因超像素边界不准确导致部分像素误分类，限制了整体性能。本文提出一种新型图加权对比学习方法，避免使用超像素分割，直接通过神经网络学习高光谱图像表征。此外，不同于需全图节点参与训练的方法，本方法支持小批量训练，仅需处理部分节点，降低计算复杂度并提升对未见节点的泛化能力。在三个常用数据集上的实验表明，该方法显著优于依赖超像素分割的基线模型。
原文摘要 · Abstract (English)
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.
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