Estimation of number of signal subspaces in hyperspectral imagery using low-rank subspace representation

摘要

In this paper, we address the problem of hyperspectral subspace estimation based on low-rank representation. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces. In particular, we develop simple estimation approaches without user-defined parameters. Real data experiments demonstrate excellent performance of the proposed approaches.