MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition

TL;DR

This paper introduces MultiHU-TD, an interpretable tensor decomposition framework for hyperspectral unmixing that integrates multiple features and constraints, improving interpretability and performance on real data.

Contribution

It presents a novel low-rank multifeature hyperspectral unmixing method based on tensor decomposition with an integrated abundance sum-to-one constraint and enhanced interpretability.

Findings

Effective unmixing on real hyperspectral images

Enhanced interpretability through mathematical and graphical analysis

Incorporation of additional features like morphology and neighborhood patches

Abstract

Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework. Tensor models such as canonical polyadic decomposition allow for this kind of unmixing but lack a general framework and interpretability of the results. In this article, we propose an interpretable methodological framework for low-rank multifeature hyperspectral unmixing based on tensor decomposition (MultiHU-TD) that incorporates the abundance sum-to-one constraint in the alternating optimization alternating direction method of multipliers (ADMM) algorithm and provide in-depth mathematical, physical, and graphical interpretation and connections with the extended linear mixing model. As additional features, we propose to incorporate mathematical morphology and reframe a previous work on neighborhood patches within MultiHU-TD. Experiments on real HSIs showcase the interpretability of the model and the analysis of the results. Python and MATLAB implementations are made available on GitHub.