Unmixing methods based on nonnegativity and weakly mixed pixels for astronomical hyperspectral datasets

TL;DR

This paper introduces a hybrid blind source separation method combining nonnegative matrix factorization and Sparse Component Analysis to improve unmixing of astronomical hyperspectral images, addressing issues of non-uniqueness and real-world data imperfections.

Contribution

The paper proposes a novel hybrid unmixing approach that initializes NMF with SCA estimates, enhancing solution uniqueness and robustness for astronomical hyperspectral data.

Findings

Hybrid method improves unmixing accuracy on synthetic data.

Initialization with SCA constrains NMF convergence effectively.

Approach shows potential for real astronomical data applications.

Abstract

[Abridged] An increasing number of astronomical instruments (on Earth and space-based) provide hyperspectral images, that is three-dimensional data cubes with two spatial dimensions and one spectral dimension. The intrinsic limitation in spatial resolution of these instruments implies that the spectra associated with pixels of such images are most often mixtures of the spectra of the "pure" components that exist in the considered region. In order to estimate the spectra and spatial abundances of these pure components, we here propose an original blind signal separation (BSS), that is to say an unsupervised unmixing method. Our approach is based on extensions and combinations of linear BSS methods that belong to two major classes of methods, namely nonnegative matrix factorization (NMF) and Sparse Component Analysis (SCA). The former performs the decomposition of hyperspectral images, as a set of pure spectra and abundance maps, by using nonnegativity constraints, but the estimated solution is not unique: It highly depends on the initialization of the algorithm. The considered SCA methods are based on the assumption of the existence of points or tiny spatial zones where only one source is active (i.e., one pure component is present). In real conditions, the assumption of perfect single-source points or zones is not always realistic. In such conditions, SCA yields approximate versions of the unknown sources and mixing coefficients. We propose to use part of these preliminary estimates from the SCA to initialize several runs of the NMF to constrain the convergence of the NMF algorithm. Detailed tests with synthetic data show that the decomposition achieved with such hybrid methods is nearly unique and provides good performance, illustrating the potential of applications to real data.