Noise Reduction in Hyperspectral Imagery: Overview and Application
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, q...
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doaj-bfd1aa82e7e040af8223e2ae56960f7e2020-11-24T22:23:53ZengMDPI AGRemote Sensing2072-42922018-03-0110348210.3390/rs10030482rs10030482Noise Reduction in Hyperspectral Imagery: Overview and ApplicationBehnood Rasti0Paul Scheunders1Pedram Ghamisi2Giorgio Licciardi3Jocelyn Chanussot4Keilir Institute of Technology (KIT), Grænásbraut 910, 235 Reykjanesbær, Iceland; The Department of Electrical and Computer Engineering, University of Iceland, Sæmundargata 2, 101 Reykjavik, IcelandVisionlab, University of Antwerp (CDE) Universiteitsplein 1 (N Building), B-2610 Antwerp, BelgiumGerman Aerospace Center (DLR), Earth Observation Center, Remote Sensing Technology Institute, SAR Signal Processing, Oberpfaffenhofen, 82234 Wessling, GermanyHypatia Research Consortium, 00133 Roma, ItalyGIPSA-lab, Grenoble INP, CNRS, University Grenoble Alpes, 38000 Grenoble, FranceHyperspectral 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.http://www.mdpi.com/2072-4292/10/3/482classificationdenoisinghyperspectral imaginghyperspectral remote sensingimage analysisimage processinginverse problemslow-ranknoise reductionremote sensingrestorationsparsitysparse modelingspectroscopy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Behnood Rasti Paul Scheunders Pedram Ghamisi Giorgio Licciardi Jocelyn Chanussot |
spellingShingle |
Behnood Rasti Paul Scheunders Pedram Ghamisi Giorgio Licciardi Jocelyn Chanussot Noise Reduction in Hyperspectral Imagery: Overview and Application Remote Sensing classification denoising hyperspectral imaging hyperspectral remote sensing image analysis image processing inverse problems low-rank noise reduction remote sensing restoration sparsity sparse modeling spectroscopy |
author_facet |
Behnood Rasti Paul Scheunders Pedram Ghamisi Giorgio Licciardi Jocelyn Chanussot |
author_sort |
Behnood Rasti |
title |
Noise Reduction in Hyperspectral Imagery: Overview and Application |
title_short |
Noise Reduction in Hyperspectral Imagery: Overview and Application |
title_full |
Noise Reduction in Hyperspectral Imagery: Overview and Application |
title_fullStr |
Noise Reduction in Hyperspectral Imagery: Overview and Application |
title_full_unstemmed |
Noise Reduction in Hyperspectral Imagery: Overview and Application |
title_sort |
noise reduction in hyperspectral imagery: overview and application |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-03-01 |
description |
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. |
topic |
classification denoising hyperspectral imaging hyperspectral remote sensing image analysis image processing inverse problems low-rank noise reduction remote sensing restoration sparsity sparse modeling spectroscopy |
url |
http://www.mdpi.com/2072-4292/10/3/482 |
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