K-Means Embedded Deep Transform Learning for Hyperspectral Band Selection

In clustering-based hyperspectral band selection techniques, 2-D images of each band are usually taken as input samples. Some form of feature extraction on these images is performed before they are input to the clustering algorithm. The clustering algorithm returns the cluster centroids; the bands closest to the centroids are selected as representative bands for each cluster. In this work, we propose a joint representation learning and clustering framework. We embed the popular $K$ -means clustering loss into the newly developing framework of deep transform learning and solve the ensuing formulation via alternating direction method of multipliers (ADMM). We combine clustering with feature extraction. Application of our proposed solution to the hyperspectral band selection problem shows that we improve over the state of the art by a reasonable margin.