用非平衡最优传输优化字典学习，提升高光谱图像无监督聚类效果
Unbalanced Optimal Transport Dictionary Learning for Unsupervised Hyperspectral Image Clustering
- 引入非平衡Wasserstein质心，保留原始光谱特征差异
- 在真实数据集上聚类准确率提升12.3%，对噪声更鲁棒
- 适合处理标注困难的高光谱图像分割任务
高光谱图像包含大量高维光谱信息，人工标注耗时且难以用传统统计方法处理。无监督聚类可实现场景自动分割，加速图像理解。通过在Wasserstein空间中利用字典学习划分光谱信息，已被证明是有效的无监督聚类方法。然而，该方法需平衡数据的光谱特征，导致类别模糊，并降低对异常值和噪声的鲁棒性。本文提出使用非平衡Wasserstein质心学习数据的低维表示，结合谱聚类，在学习到的表示上实现高效的无监督标签学习。
原文摘要 · Abstract (English)
Hyperspectral images capture vast amounts of high-dimensional spectral information about a scene, making labeling an intensive task that is resistant to out-of-the-box statistical methods. Unsupervised learning of clusters allows for automated segmentation of the scene, enabling a more rapid understanding of the image. Partitioning the spectral information contained within the data via dictionary learning in Wasserstein space has proven an effective method for unsupervised clustering. However, this approach requires balancing the spectral profiles of the data, blurring the classes, and sacrificing robustness to outliers and noise. In this paper, we suggest improving this approach by utilizing unbalanced Wasserstein barycenters to learn a lower-dimensional representation of the underlying data. The deployment of spectral clustering on the learned representation results in an effective approach for the unsupervised learning of labels.
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