  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.
