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Noise Reduction in Hyperspectral Images through Spectral Unmixing

Cerra, Daniele and Müller, Rupert and Reinartz, Peter (2014) Noise Reduction in Hyperspectral Images through Spectral Unmixing. IEEE Geoscience and Remote Sensing Letters, 11 (1), pp. 109-113. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/LGRS.2013.2247562 ISBN 1545-598X ISSN 1545-598X

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488723


Spectral unmixing and denoising of hyperspectral images have always been regarded as separate problems. By considering the physical properties of a mixed spectrum, this letter introduces unmixing-based denoising, a supervised methodology representing any pixel as a linear combination of reference spectra in a hyperspectral scene. Such spectra are related to some classes of interest, and exhibit negligible noise influences, as they are averaged over areas for which ground truth is available. After the unmixing process, the residual vector is mostly composed by the contributions of uninteresting materials, unwanted atmospheric influences and sensor-induced noise, and is thus ignored in the reconstruction of each spectrum. The proposed method, in spite of its simplicity, is able to remove noise effectively for spectral bands with both low and high signal-to-noise ratio. Experiments show that this method could be used to retrieve spectral information from corrupted bands, such as the ones placed at the edge between ultraviolet and visible light frequencies, which are usually discarded in practical applications. The proposed method achieves better results in terms of visual quality in comparison to competitors, if the mean squared error is kept constant. This leads to questioning the validity of mean squared error as a predictor for image quality in remote sensing applications.

Item URL in elib:https://elib.dlr.de/82470/
Document Type:Article
Title:Noise Reduction in Hyperspectral Images through Spectral Unmixing
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cerra, Danieledaniele.cerra (at) dlr.deUNSPECIFIED
Müller, Rupertrupert.mueller (at) dlr.deUNSPECIFIED
Reinartz, PeterPeter.Reinartz (at) dlr.deUNSPECIFIED
Date:January 2014
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2013.2247562
Page Range:pp. 109-113
Gamba, PaoloUniversity of Pavia; Pavia, Italy
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:IEEE Geoscience and Remote Sensing Letters
Keywords:Denoising, hyperspectral images, image restoration, mean squared error, spectral unmixing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Cerra, Daniele
Deposited On:31 May 2013 12:30
Last Modified:08 Mar 2018 18:30

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