Reweighted sparse unmixing for hyperspectral images with noise level estimation

Abstract

Spectral unmixing plays an important role in hyperspectral image applications and an amount of work has been devoted to hyperspectral unmixing (HU) in recent years. Hyperspectral images (HSIs) are typically corrupted by mixed types of noise and the noise level usually varies among different bands. The unmixing results could be misleading without individualized noise estimations for each band. In addition, there exist conspicuous sparsity of endmembers and spatial correlation in hyperspectral scenes, which can be utilized to improve the hyperspectral unmixing performance. In this paper, we propose a novel model that integrates weighted sparse unmixing with bandwise noise characterization. To deal with various noise present in HSIs, we introduce a weighting matrix derived from the Gaussian noise estimation in each band and a l1 regularizer for sparse noise. Then we incorporate a weighted sparse regularizer, which includes both spatial and spectral signatures weighting factors to take advantage of spatial correlation among fractional abundances and enhance the sparsity of endmembers simultaneously. An efficient algorithm based on alternating direction method of multipliers (ADMM) is utilized to solve the proposed model. The experimental results on synthetic and real hyperspectral data suggest that the consolidation of noise estimation and weighted regularizer could achieve superior unmixing results compared with other advanced unmixing methods.