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SULoRA: Subspace Unmixing with Low-Rank Attribute Embedding for Hyperspectral Data Analysis

Hong, Danfeng and Zhu, Xiao Xiang (2018) SULoRA: Subspace Unmixing with Low-Rank Attribute Embedding for Hyperspectral Data Analysis. IEEE Journal of Selected Topics in Signal Processing, 12 (6), pp. 1351-1363. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTSP.2018.2877497 ISSN 1932-4553

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Official URL: https://ieeexplore.ieee.org/document/8502105

Abstract

To support high-level analysis of spaceborne imaging spectroscopy (hyperspectral) imagery, spectral unmixing has been gaining significance in recent years. However, from the inevitable spectral variability, caused by illumination and topography change, atmospheric effects and so on, makes it difficult to accurately estimate abundance maps in spectral unmixing. Classical unmixing methods, e.g. linear mixing model (LMM), extended linear mixing model (ELMM), fail to robustly handle this issue, particularly facing complex spectral variability. To this end, we propose a subspace-based unmixing model using low-rank learning strategy, called subspace unmixing with low-rank attribute embedding (SULoRA), robustly against spectral variability in inverse problems of hyperspectral unmixing. Unlike those previous approaches that unmix the spectral signatures directly in original space, SULoRA is a general subspace unmixing framework that jointly estimates subspace projections and abundance maps in order to find a ‘raw’ subspace which is more suitable for carrying out the unmixing procedure. More importantly, we model such ‘raw’ subspace with low-rank attribute embedding. By projecting the original data into a low-rank subspace, SULoRA can effectively address various spectral variabilities in spectral unmixing. Furthermore, we adopt an alternating direction method of multipliers (ADMM) based to solve the resulting optimization problem. 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/122305/
Document Type:Article
Title:SULoRA: Subspace Unmixing with Low-Rank Attribute Embedding for Hyperspectral Data Analysis
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:December 2018
Journal or Publication Title:IEEE Journal of Selected Topics in Signal Processing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI :10.1109/JSTSP.2018.2877497
Page Range:pp. 1351-1363
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1932-4553
Status:Published
Keywords:Alternating direction method of multipliers, hyperspectral data analysis, low-rank attribute embedding, remote sensing, subspace 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:19 Oct 2018 13:06
Last Modified:14 May 2020 10:35

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