Spatial-Spectral Nonlinear Hyperspectral Unmixing Under Complex Noise

Abstract

Generalized bilinear model (GBM) has emerged as a representative nonlinear model in hyperspectral image (HSI) analysis. However, the noise in GBM is presumed to be Gaussian. In addition, conventional GBM based unmixing approaches cannot fully utilize the spatial information in HSI. To this end, we propose a spatial-spectral nonlinear hyperspectral unmixing approach under complex noise (SSHUCN) including Gaussian noise, impulse noise, stripes, etc. First, we perform superpixel segmentation (SS) on the first component of HSI to make good use of the spatial information, which can get different homogeneous regions termed as superpixels. Then, we adopt the maximum a posteriori framework to unmix each superpixel in the HSI, and the corresponding optimization objective function is solved via the alternative direction method of multipliers (ADMM). A large number of experiments have verified the superiority of SSHUCN using synthetic and real HSIs.