2014/12/15 by Yoann Altmann, Altmann, Yoann, Marcelo Pereyra +3
Chemistry · Earth and Planetary Sciences · Engineering · #Advanced Image Fusion Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Remote Sensing and Land Use #Remote-Sensing Image Classification #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.1412.4681
openalex publication_date 2014/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a new Bayesian model and algorithm for nonlinear unmixing\nof hyperspectral images. The model proposed represents the pixel reflectances\nas linear combinations of the endmembers, corrupted by nonlinear (with respect\nto the endmembers) terms and additive Gaussian noise. Prior knowledge about the\nproblem is embedded in a hierarchical model that describes the dependence\nstructure between the model parameters and their constraints. In particular, a\ngamma Markov random field is used to model the joint distribution of the\nnonlinear terms, which are expected to exhibit significant spatial\ncorrelations. An adaptive Markov chain Monte Carlo algorithm is then proposed\nto compute the Bayesian estimates of interest and perform Bayesian inference.\nThis algorithm is equipped with a stochastic optimisation adaptation mechanism\nthat automatically adjusts the parameters of the gamma Markov random field by\nmaximum marginal likelihood estimation. Finally, the proposed methodology is\ndemonstrated through a series of experiments with comparisons using synthetic\nand real data and with competing state-of-the-art approaches.\n