Nonlinear spectral unmixing of hyperspectral images using Gaussian processes

TL;DR

This paper introduces an unsupervised nonlinear spectral unmixing method for hyperspectral images using Gaussian processes, estimating abundances and nonlinear functions without prior spectral signature knowledge.

Contribution

It proposes a novel Bayesian framework that jointly estimates abundance vectors and nonlinear functions, advancing hyperspectral unmixing techniques.

Findings

Effective on synthetic data

Performs well on real hyperspectral images

Outperforms some existing methods

Abstract

This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method consists of the Bayesian estimation of the abundance vectors for all the image pixels and the nonlinear function relating the abundance vectors to the observations. The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is evaluated with simulations conducted on synthetic and real data.