用深度自编码与超像素扩散图提升无监督高光谱聚类精度
Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering
- 先用掩码自编码器学习去噪特征，融合空间与光谱信息
- 在压缩特征空间构建带空间正则的扩散图，提升数据流形建模能力
- 在Botswana和KSC数据集上表现优于现有方法，适合遥感图像聚类
本文提出一种无监督高光谱图像聚类框架，结合掩码深度表示学习与基于扩散的聚类，扩展了空间正则超像素扩散学习（S²DL）算法。首先，通过带有视觉变换器骨干的无监督掩码自编码器（UMAE）学习原始高光谱图像的去噪潜在表示，该模型考虑空间上下文与长程光谱相关性，并利用少量训练像素实现高效预训练。随后，采用熵率超像素（ERS）算法将图像分割为超像素，并在压缩潜在空间中使用欧氏距离与扩散距离构建空间正则化扩散图，而非直接在高光谱空间中操作。所提算法DS²DL利用更精确的扩散距离，使扩散图构建更贴近数据流形的内在几何结构，从而提升标签准确率与聚类质量。在Botswana和KSC数据集上的实验验证了其有效性。
原文摘要 · Abstract (English)
An unsupervised framework for hyperspectral image (HSI) clustering is proposed that incorporates masked deep representation learning with diffusion-based clustering, extending the Spatially-Regularized Superpixel-based Diffusion Learning ($S^2DL$) algorithm. Initially, a denoised latent representation of the original HSI is learned via an unsupervised masked autoencoder (UMAE) model with a Vision Transformer backbone. The UMAE takes spatial context and long-range spectral correlations into account and incorporates an efficient pretraining process via masking that utilizes only a small subset of training pixels. In the next stage, the entropy rate superpixel (ERS) algorithm is used to segment the image into superpixels, and a spatially regularized diffusion graph is constructed using Euclidean and diffusion distances within the compressed latent space instead of the HSI space. The proposed algorithm, Deep Spatially-Regularized Superpixel-based Diffusion Learning ($DS^2DL$), leverages more faithful diffusion distances and subsequent diffusion graph construction that better reflect the intrinsic geometry of the underlying data manifold, improving labeling accuracy and clustering quality. Experiments on Botswana and KSC datasets demonstrate the efficacy of $DS^2DL$.
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