Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery

Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a <inline-formula><math xmlns="http://www.w3.org/1998...

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Bibliographic Details
Main Authors: John Waczak, David J. Lary
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Remote Sensing
Subjects:
endmember extraction
spectral unmixing
hyperspectral imaging
unsupervised machine learning
source apportionment
Online Access:https://www.mdpi.com/2072-4292/16/22/4316
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https://www.mdpi.com/2072-4292/16/22/4316

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