  Subspace clustering techniques have shown promise in hyperspectral image
segmentation. The fundamental assumption in subspace clustering is that the
samples belonging to different clusters/segments lie in separable subspaces.
What if this condition does not hold? We surmise that even if the condition
does not hold in the original space, the data may be nonlinearly transformed to
a space where it will be separable into subspaces. In this work, we propose a
transformation based on the tenets of deep dictionary learning (DDL). In
particular, we incorporate the sparse subspace clustering (SSC) loss in the DDL
formulation. Here DDL nonlinearly transforms the data such that the transformed
representation (of the data) is separable into subspaces. We show that the
proposed formulation improves over the state-of-the-art deep learning
techniques in hyperspectral image clustering.
