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Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling

Rasti, Behnood and Ulfarsson, Magnus Orn and Ghamisi, Pedram (2017) Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling. IEEE Geoscience and Remote Sensing Letters, 14 (12), pp. 2335-2339. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/LGRS.2017.2764059 ISSN 1545-598X

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Official URL: http://ieeexplore.ieee.org/document/8098642/

Abstract

Hyperspectral restoration is a preprocessing step for hyperspectral imagery. In this letter, we propose a parameter-free method for the restoration of hyperspectral images (HSIs) called HyRes. The restoration method is based on a sparse low-rank model that uses the ℓ1 penalized least squares for estimating the unknown signal. The Stein's unbiased risk estimator is exploited to select all the parameters of the model yielding a fully automatic (parameter free) technique. Experimental results confirm that HyRes outperforms the state-of-the-art techniques in terms of signal-to-noise ratio, structural similarity index, and spectral angle distance for a simulated data set and in terms of noise-level estimation for the real data sets used in this letter. In the experiments, it was noted that HyRes is computationally less expensive compared with competitive techniques. Therefore, HyRes can be used as a reliable automatic preprocessing step for further analysis of HSIs.

Item URL in elib:https://elib.dlr.de/118210/
Document Type:Article
Title:Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Rasti, BehnoodKeilir Institute of TechnologyUNSPECIFIED
Ulfarsson, Magnus OrnUniversity of Iceland, ReykjavikUNSPECIFIED
Ghamisi, PedramPedram.Ghamisi (at) dlr.deUNSPECIFIED
Date:December 2017
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI :10.1109/LGRS.2017.2764059
Page Range:pp. 2335-2339
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Hyperspectral restoration, hyperspectral imagery, parameter-free method Hyres, sparse low-rank model
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, Vorhaben Optical Remote Sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Zielske, Mandy
Deposited On:12 Jan 2018 15:11
Last Modified:08 Mar 2018 18:31

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