Dictionary learning and sparse coding have been widely studied as mechanisms for unsupervised feature learning. Unsupervised learning could bring enormous benefit to the processing of hyperspectral images and to other remote sensing data analysis because labelled data are often scarce in this field. We propose a method for clustering the pixels of hyperspectral images using sparse coefficients computed from a representative dictionary as features. We show empirically that the proposed method works more effectively than clustering on the original pixels. We also demonstrate that our approach, in certain circumstances, outperforms the clustering results of features extracted using principal component analysis and non-negative matrix factorisation. Furthermore, our method is suitable for applications in repetitively clustering an ever-growing amount of high-dimensional data, which is the case when working with hyperspectral satellite imagery.

中文翻译：

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用于高光谱图像聚类的字典学习

字典学习和稀疏编码作为无监督特征学习的机制已被广泛研究。无监督学习可以为高光谱图像的处理和其他遥感数据分析带来巨大的好处，因为该领域的标记数据通常很稀缺。我们提出了一种使用从代表性字典计算出的稀疏系数作为特征来聚类高光谱图像像素的方法。我们凭经验表明，所提出的方法比对原始像素进行聚类更有效。我们还证明，在某些情况下，我们的方法优于使用主成分分析和非负矩阵分解提取的特征的聚类结果。此外，