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Blind Hyperspectral Unmixing using Autoencoders : A Critical Comparison

Blind Hyperspectral Unmixing using Autoencoders : A Critical Comparison


Title: Blind Hyperspectral Unmixing using Autoencoders : A Critical Comparison
Author: Pálsson, Burkni
Sveinsson, Jóhannes Rúnar
Úlfarsson, Magnús Örn
Date: 2022-01-06
Language: English
Scope: 32
Department: Faculty of Electrical and Computer Engineering
Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing; 15()
ISSN: 1939-1404
DOI: https://doi.org/10.1109/JSTARS.2021.3140154
Subject: Myndvinnsla; Fjarkönnun; Litrófsgreining; Líkanagerð; Atmospheric modeling; autoencoder; deep learning; Feature extraction; Hyperspectral data unmixing; Hyperspectral imaging; image processing; multitask learning; neural network; Object detection; Sparse matrices; Spatial resolution; spectral-spatial model; Spectroscopy; Computers in Earth Sciences; Atmospheric Science
URI: https://hdl.handle.net/20.500.11815/3036

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Citation:

Pálsson , B , Sveinsson , J R & Úlfarsson , M Ö 2022 , ' Blind Hyperspectral Unmixing using Autoencoders : A Critical Comparison ' , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15 , pp. 1340-1372 . https://doi.org/10.1109/JSTARS.2021.3140154

Abstract:

Deep learning has shown to be a powerful tool and has heavily impacted the data-intensive field of remote sensing. As a result, the number of published deep learning-based spectral unmixing techniques is proliferating. Blind hyperspectral unmixing (HU) is the process of resolving the measured spectrum of a pixel into a combination of a set of spectral signatures called endmembers and simultaneously determining their fractional abundances in the pixel. This paper details the various autoencoder architectures used in HU and provides a critical comparison of some of the existing published blind unmixing methods based on autoencoders. Eleven different autoencoder methods and one traditional method will be compared in blind unmixing experiments using four real datasets and four synthetic datasets with different spectral variability. Additionally, extensive ablation experiments with a simple spectral unmixing autoencoder will be performed. The results are interpreted in terms of the various implementation details, and the question of why autoencoder methods are so powerful compared to traditional methods is unraveled. The source codes for all methods implemented in this paper can be found at the following location: https://github.com/burknipalsson/hu_autoencoders.

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