[2603.20263] MiSiSUn: Minimum Simplex Semisupervised Unmixing

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2603.20263 (eess) [Submitted on 13 Mar 2026] Title:MiSiSUn: Minimum Simplex Semisupervised Unmixing Authors:Behnood Rasti, Bikram Koirala, Paul Scheunders View a PDF of the paper titled MiSiSUn: Minimum Simplex Semisupervised Unmixing, by Behnood Rasti and 2 other authors View PDF HTML (experimental) Abstract:This paper proposes a semisupervised geometric unmixing approach called minimum simplex semisupervised unmixing (MiSiSUn). The geometry of the data was incorporated for the first time into library-based unmixing using a simplex-volume-flavored penalty based on an archetypal analysis-type linear model. The experimental results were performed on two simulated datasets considering different levels of mixing ratios and spatial instruction at varying input noise. MiSiSUn considerably outperforms state-of-the-art semisupervised unmixing methods. The improvements vary from 1 dB to over 3 dB in different scenarios. The proposed method was also applied to a real dataset where visual interpretation is close to the geological map. MiSiSUn was implemented using PyTorch, which is open-source and available at this https URL. Moreover, we provide a dedicated Python package for Semisupervised Unmixing, which is open-source and includes all the methods used in the experiments for the sake of reproducibility. Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Mach...