Tensorial Multiview Subspace Clustering for Polarimetric Hyperspectral Images

Polarimetric hyperspectral images (PHSIs) can provide complementary representations of a scene from the perspectives of images, spectra, and polarization at the same time and are expected to improve the quality of scene description. In this article, the clustering for PHSI is deemed to be a multiview clustering task, and a tensorial polarimetric-spectral multiview subspace clustering (TPS-MSC) algorithm for PHSI is proposed. It constructs a small size dictionary, instead of a large self-representative dictionary, by pre-clustering each view independently to give a sparse representation of all the data. Then, the view-specific representation matrices are tensorized to explore the low-rank structure among different views, and the consistency of all views in pre-clustering is incorporated into the representation learning framework to strengthen the interview correlations. The proposed model is efficiently optimized by the alternative direction minimization of multipliers (ADMM) algorithm. Some experiments are carried out to validate the capacity of PHSI for target identification and to demonstrate the accuracy and efficiency of the proposed TPS-MSC algorithm.