Using Spatial Correlation in Semi-Supervised Hyperspectral Unmixing under Polynomial Post-nonlinear Mixing Model

TL;DR

This paper introduces a semi-supervised hyperspectral unmixing method that leverages spatial correlation and a polynomial post-nonlinear mixing model, using Bayesian inference and MCMC to improve abundance estimation with limited labeled data.

Contribution

It proposes a novel semi-supervised unmixing approach that incorporates spatial information and a polynomial post-nonlinear model, utilizing a sparse Dirichlet prior and Bayesian inference.

Findings

Effective on simulated data, demonstrating improved abundance estimation.

Utilizes a large spectral library without needing exact material labels.

Models spatial correlation with Markov Random Fields.

Abstract

This paper presents a semi-supervised hyperspectral unmixing solution that integrate the spatial information in the abundance estimation procedure. The proposed method is applied on a nonlinear model based on polynomial postnonlinear mixing model where characterizes each pixel reflections composed of nonlinear function of pure spectral signatures added by noise. We partitioned the image to classes where contains similar materials so share the same abundance vector. The spatial correlation between pixels belonging to each class is modelled by Markov Random Field. A Bayesian framework is proposed to estimate the classes and corresponding abundance vectors alternatively. We proposed sparse Dirichlet prior for abundance vector that made it possible to use this algorithm in semi-supervised scenario where the exact involved materials are unknown. In this approach, we just need to have a large library of pure spectral signatures including the desired materials. An MCMC algorithm is used to estimate the abundance vector based on generated samples. The result of implementation on simulated data shows the prominence of proposed approach.