Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

TL;DR

This paper reviews advanced and conventional hyperspectral unmixing methods, compares their performance on various datasets, and introduces the HySUPP Python package for reproducibility and further research.

Contribution

It provides a comprehensive overview and critical comparison of unmixing techniques, and introduces an open-source Python package for hyperspectral unmixing.

Findings

Different unmixing categories excel in different scenarios.

Advanced techniques outperform conventional ones in certain cases.

The HySUPP package facilitates reproducibility and method comparison.

Abstract

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers, linear unmixing can be divided into three main groups: supervised, semi-supervised, and unsupervised (blind) linear unmixing. Advances in Image processing and machine learning substantially affected unmixing. This paper provides an overview of advanced and conventional unmixing approaches. Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories. We compare the performance of the unmixing techniques on three simulated and two real datasets. The experimental results reveal the advantages of different unmixing categories for different unmixing scenarios. Moreover, we provide an open-source Python-based package available at https://github.com/BehnoodRasti/HySUPP to reproduce the results.