Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
It addresses the problem of spectral unmixing for researchers in remote sensing and image processing, but is incremental as it focuses on reviewing and comparing existing methods.
This paper provides an overview and comparison of conventional and advanced linear unmixing techniques for hyperspectral data, evaluating their performance on simulated and real datasets to highlight advantages in different scenarios.
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.