Bilinear normal mixing model for spectral unmixing. Issue 2 (1st February 2019)

• Record Type: Journal Article
• Authors: Luo, Wenfei; Gao, Lianru; Zhang, Ruihao; Marinoni, Andrea; Zhang, Bing
• Is Part Of: IET image processing. Volume 13:Issue 2(2019)
• Page Start: 344
• Page End: 354
• Publication Date: 2019-02-01
• Subjects: polynomials -- geophysical image processing -- remote sensing -- hyperspectral imaging -- Monte Carlo methods -- spectral analysis -- feature extraction -- normal distribution; bilinear normal mixing model -- spectral unmixing -- SU -- hyperspectral remote sensing image analysis -- spectral variance -- nonlinearity -- photon multiple‐scattering -- BNMM -- normal distribution model -- endmembers variability -- Hamiltonian Monte Carlo algorithm -- endmember extraction algorithms -- unmixing algorithms -- polynomial postnonlinear mixing model; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5458
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Spectral unmixing (SU) is a useful tool for hyperspectral remote sensing image analysis. However, due to the interference of spectral variance and non‐linearity caused by photon multiple‐scattering, the result might be an inaccuracy. In addition, the unmixing performance of typically relies on the prior knowledge of endmembers. Although many classical endmember extraction algorithms have been presented, it is hard to obtain accurate endmembers in practical applications. This study presents a bilinear normal mixing model named as BNMM to tackle these issues. In fact, BNMM employs the polynomial post‐non‐linear mixing model to alleviate the effect of non‐linearity and uses a normal distribution model to reduce the influence of endmembers variability. Based on the BNMM, the authors develop a Hamiltonian Monte Carlo algorithm for SU. The experimental results demonstrate that the proposed algorithm outperforms other classical unmixing algorithms in the case of simulated and benchmark datasets.