access icon free Bilinear normal mixing model for spectral unmixing

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.

Inspec keywords: geophysical image processing; feature extraction; normal distribution; remote sensing; hyperspectral imaging; Monte Carlo methods; polynomials; spectral analysis

Other keywords: Hamiltonian Monte Carlo algorithm; photon multiple-scattering; bilinear normal mixing model; endmember extraction algorithms; nonlinearity; spectral variance; hyperspectral remote sensing image analysis; unmixing algorithms; BNMM; endmembers variability; spectral unmixing; normal distribution model; polynomial postnonlinear mixing model; SU

Subjects: Monte Carlo methods; Geography and cartography computing; Image recognition; Geophysics computing; Data and information; acquisition, processing, storage and dissemination in geophysics; Geophysical techniques and equipment; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Monte Carlo methods; Probability theory, stochastic processes, and statistics; Computer vision and image processing techniques

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5458
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