An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images

Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a complement to spectral information, but related research only focus on the clustering for HSIs without considering polarization, and cannot effectively process large-scale hyperspectral datasets. In this paper, we propose an efficient representation-based subspace clustering framework for polarized hyperspectral images (PHSIs). Combining with spectral information and polarized information, this framework is extensible for most existing representation-based subspace clustering algorithms. In addition, with a sampling-clustering-classification strategy which firstly clusters selected in-sample data into several classes and then matches the out-of-sample data into these classes by collaborative representation-based classification, the proposed framework significantly reduces the computational complexity of clustering algorithms for PHSIs. Some experiments were carried out to demonstrate the accuracy, efficiency and potential capabilities of the algorithms under the proposed framework.