Bayesian Unmixing using Sparse Dirichlet Prior with Polynomial\n Post-nonlinear Mixing Model

2018/03/02 by Fahime Amiri, Amiri, Fahime, Mohammad Hossein Kahaei +1
Chemistry · Computer Science · Engineering · #FOS: Electrical engineering #Image and Signal Denoising Methods #Remote-Sensing Image Classification #Signal Processing (eess.SP) #Spectroscopy and Chemometric Analyses #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1803.02670

openalex publication_date 2018/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A sparse Dirichlet prior is proposed for estimating the abundance vector of\nhyperspectral images with a nonlinear mixing model. This sparse prior is led to\nan unmixing procedure in a semi-supervised scenario in which exact materials\nare unknown. The nonlinear model is a polynomial post-nonlinear mixing model\nthat represents each hyperspectral pixel as a nonlinear function of pure\nspectral signatures corrupted by additive white noise. Simulation results show\nmore than 50% improvement in the estimation error.\n

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