Normal Endmember Spectral Unmixing Method for Hyperspectral Imagery

Abstract

The normal compositional model (NCM) has been introduced to characterize mixed pixels in hyperspectral images, particularly when endmember variability needs to be considered in the unmixing process. Each pixel is modeled as a linear combination of endmembers, which are treated as Gaussian random variables in order to capture such spectral variability. Since the combination coefficients (i.e., abundances) and the endmembers are unknown variables at the same time in the NCM, the parameter estimation is more difficult in comparison with conventional approaches. In order to address this issue, we propose a new Bayesian method, termed normal endmember spectral unmixing (NESU), for improved parameter estimation in this context. It considers the endmembers as known variables (resulting from the extraction of endmember bundles), then performs optimal estimations of the remaining unknown parameters, i.e., the abundances, using Bayesian inference. The particle swarm optimization (PSO) technique is adopted to estimate the optimal values of abundances according to their posterior probabilities. The performance of the proposed algorithm is evaluated using both synthetic and real hyperspectral data. The obtained results demonstrate that the proposed method leads to significant improvements in terms of unmixing accuracies.