Residual component analysis of hyperspectral images -- Application to joint nonlinear unmixing and nonlinearity detection

TL;DR

This paper introduces a Bayesian nonlinear unmixing model for hyperspectral images that detects nonlinearity and segments the image based on spatial properties, validated on synthetic and real data.

Contribution

It proposes a joint nonlinear unmixing and nonlinearity detection method using a Markov random field and Bayesian estimation, advancing hyperspectral image analysis.

Findings

Accurate nonlinearity detection in synthetic data.

Effective unmixing and segmentation in real hyperspectral images.

Bayesian approach outperforms existing methods.

Abstract

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the endmembers and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. The performance of the proposed strategy is first evaluated on synthetic data. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.