A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery

Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have become very popular. However, such models are often based on a local averaging constraint, which does not allow for a detailed exploration of the spatial information, thus limiting their discriminative capability and preventing the spatial homogeneity of the clustering results. To address these relevant issues, in this letter, we develop a new and effective l(2)-norm regularized SSC algorithm which adds a four-neighborhood l(2)-norm regularizer into the classical SSC model, thus taking full advantage of the spatial-spectral information contained in HSIs. The experimental results confirm the potential of including the spatial information (through the newly added l(2)-norm regularization term) in the SSC framework, which leads to a significant improvement in the clustering accuracy of SSC when applied to HSIs.