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Endmember Bundle Extraction based on Multi-objective Optimization

Liu, Rong and Zhu, Xiao Xiang (2021) Endmember Bundle Extraction based on Multi-objective Optimization. IEEE Transactions on Geoscience and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3037249. ISSN 0196-2892. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9268973

Abstract

A number of endmember extraction methods have been developed to identify pure pixels in hyperspectral images (HSIs). The majority of them use only one spectrum to represent one kind of material, which ignores the spectral variability problem that particularly characterizes a HSI with high spatial resolution. Only a few algorithms have been developed to identify multiple endmembers representing the spectral variability within each class, called endmember bundle extraction (EBE). This article introduces multiobjective particle swarm optimization for the identification of multiple endmember spectra with variability. Unlike existing convex geometry-based EBE methods, which operate on a single geometry of the dataspace, the proposed method divides the observed data into subsets along the spectral dimension and simultaneously operates on multiple dataspaces to obtain candidate endmembers based on multiobjective particle swarm optimization. The candidate endmembers are then refined by spatial post-processing and sequential forward floating selection to produce the final result. Experiments are conducted on both synthetic and real hyperspectral data to demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/137270/
Document Type:Article
Title:Endmember Bundle Extraction based on Multi-objective Optimization
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Liu, RongRong.Liu (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2021
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2020.3037249
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:In Press
Keywords:Endmember bundle extraction, hyperspectral, multi-objective optimization, spectral variability
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Liu, Rong
Deposited On:25 Nov 2020 18:23
Last Modified:14 Jan 2021 13:51

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