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An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Hong, Danfeng and Yokoya, Naoto and Chanussot, Jocelyn and Zhu, Xiao Xiang (2019) An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing. IEEE Transactions on Image Processing, 28 (4), pp. 1923-1938. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TIP.2018.2878958 ISSN 1057-7149

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Official URL: https://ieeexplore.ieee.org/document/8528557

Abstract

Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low-coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/122568/
Document Type:Article
Title:An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Yokoya, NaotoRIKENUNSPECIFIED
Chanussot, JocelynInstitute Nationale Polytechnique de GrenobleUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:April 2019
Journal or Publication Title:IEEE Transactions on Image Processing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:28
DOI :10.1109/TIP.2018.2878958
Page Range:pp. 1923-1938
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1057-7149
Status:Published
Keywords:Alternating direction method of multipliers, low-coherent dictionary learning, remote sensing, spectral unmixing, 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 - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hong, Danfeng
Deposited On:01 Nov 2018 13:46
Last Modified:14 May 2020 10:35

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