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.

翻译：作为不受监督的特征学习机制,对词典学习和稀有编码进行了广泛研究;无监督的学习可以为超光谱图像的处理和其他遥感数据分析带来巨大好处,因为在这一领域,贴标签的数据往往很少;我们建议采用一种方法,利用从具有代表性的字典中计算出来的稀薄系数作为特征,将超光谱图像的像素组合起来;我们从经验上表明,拟议的方法比在原始像素上分组更有效;我们还表明,在某些情况下,我们的方法优于利用主要组成部分分析和非负矩阵乘法提取的特征的组合结果;此外,我们的方法适合于将越来越多的高光谱数据反复组合起来,在与超光谱卫星图像合作时就是这种情况。