Sparse unmixing of hyperspectral data with bandwise model

Abstract Sparse unmixing has long been a hot research topic in the area of hyperspectral image (HSI) analysis. Most of the traditional sparse unmixing methods usually assume to only take the Gaussian noise into consideration. However, there are also other types of noise in real HSI, i.e., impulse noise, stripes, dead lines and so on. In addition, the intensity of Gaussian noise is usually different for each band of real HSI. To this end, we propose a novel sparse unmixing method with the bandwise model (SUBM) to address the above mentioned problems simultaneously. Besides, the alternative direction method of multipliers (ADMM) is adopted for solving the proposed SUBM. Moreover, we conduct extensive experiments on synthetic and real datasets to demonstrate effectiveness of the proposed sparse unmixing method under the bandwise model.