Hyperspectral Image Unmixing Based on Sparse and Minimum Volume Constrained Nonnegative Matrix Factorization

Hyperspectal Unmixing (HU) aims at getting the endmember signature and their corresponding abundance maps from highly mixed Hyperspctral image. Nonnegative Matrix Factorization (NMF) is a widely used method for HU recently. Traditional NMF only take sparse constraint or minimum volume constraint into consideration leading to unmixing results not accurately enough. In this paper, we propose a new method based on NMF through combining volume constraint with sparse constraint. According to the convex geometry, we impose minimum volume constraint on endmember matrix. Because sparsity is nature property of abundance, we add the sparse constraint on abundance matrix. Both the experiments on synthetic and real scene images show the effectiveness of the proposed method.