论文标题
在高光谱图像上聚类的字典学习
Dictionary learning for clustering on hyperspectral images
论文作者
论文摘要
词典学习和稀疏编码已被广泛研究为无监督特征学习的机制。无监督的学习可能会给高光谱图像的处理和其他遥感数据分析带来巨大的好处，因为在该领域中标记的数据通常很少。我们提出了一种使用从代表性词典作为特征计算出的稀疏系数来聚类高光谱图像像素的方法。我们从经验上表明，所提出的方法比原始像素上的聚类更有效。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，我们的方法适用于重复聚集不断增长的高维数据的应用，在使用高光谱卫星图像时，这种情况就是这种情况。
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.
