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Noise Reduction in Hyperspectral Imagery: Overview and Application

Rasti, Behnood and Scheunders, Paul and Ghamisi, Pedram and Licciardi, Giorgio and Chanussot, Jocelyn (2018) Noise Reduction in Hyperspectral Imagery: Overview and Application. Remote Sensing, 3 (482), pp. 1-28. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs10030482 ISSN 2072-4292

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Official URL: http://www.mdpi.com/2072-4292/10/3/482

Abstract

Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a preprocessing step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a preprocessing step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a preprocessing step.

Item URL in elib:https://elib.dlr.de/119958/
Document Type:Article
Title:Noise Reduction in Hyperspectral Imagery: Overview and Application
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Rasti, BehnoodKeilir Institute of TechnologyUNSPECIFIED
Scheunders, PaulUniversity of AntwerpUNSPECIFIED
Ghamisi, PedramMF-DASUNSPECIFIED
Licciardi, GiorgioInstitute Nationale Polytechnique de GrenobleUNSPECIFIED
Chanussot, JocelynInstitute Nationale Polytechnique de GrenobleUNSPECIFIED
Date:20 March 2018
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:3
DOI :10.3390/rs10030482
Page Range:pp. 1-28
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:classification; denoising; hyperspectral imaging; hyperspectral remote sensing; image analysis; image processing; inverse problems; low-rank; noise reduction; remote sensing; restoration; sparsity; sparse modeling; spectroscopy
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 > SAR Signal Processing
Deposited By: Ghamisi, Pedram
Deposited On:24 May 2018 12:11
Last Modified:14 Dec 2019 04:26

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