Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification
This work addresses hyperspectral image classification, an incremental improvement for domain-specific applications.
The paper tackled the problem of subspace clustering in hyperspectral images when data is not separable in the original space by proposing a deep dictionary learning transformation that incorporates sparse subspace clustering loss, resulting in improved performance over state-of-the-art deep learning techniques.
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.