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Unmixing-based Denoising as a pre-processing step for Coral Reef Analysis

Cerra, Daniele and Traganos, Dimosthenis and Gege, Peter and Reinartz, Peter (2017) Unmixing-based Denoising as a pre-processing step for Coral Reef Analysis. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, XLII-1 (W1), pp. 279-282. Copernicus Publications. ISPRS Hannover Workshop: HRIGI 17, 06.-09.Juni 2017, Hannover, Germany. DOI: 10.5194/isprs-archives-XLII-1-W1-279-2017

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Official URL: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1-W1/279/2017/


Coral reefs, among the world's most biodiverse and productive submerged habitats, have faced several mass bleaching events due to climate change during the past 35 years. In the course of this century, global warming and ocean acidification are expected to cause corals to become increasingly rare on reef systems. This will result in a sharp decrease in the biodiversity of reef communities and carbonate reef structures. Coral reefs may be mapped, characterized and monitored through remote sensing. Hyperspectral images in particular excel in being used in coral monitoring, being characterized by very rich spectral information, which results in a strong discrimination power to characterize a target of interest, and separate healthy corals from bleached ones. Being submerged habitats, coral reef systems are difficult to analyse in airborne or satellite images, as relevant information is conveyed in bands in the blue range which exhibit lower signal-to-noise ratio (SNR) with respect to other spectral ranges; furthermore, water is absorbing most of the incident solar radiation, further decreasing the SNR. Derivative features, which are important in coral analysis, result greatly affected by the resulting noise present in relevant spectral bands, justifying the need of new denoising techniques able to keep local spatial and spectral features. In this paper, Unmixing-based Denoising (UBD) is used to enable analysis of a hyperspectral image acquired over a coral reef system in the Red Sea based on derivative features. UBD reconstructs pixelwise a dataset with reduced noise effects, by forcing each spectrum to a linear combination of other reference spectra, exploiting the high dimensionality of hyperspectral datasets. Results show clear enhancements with respect to traditional denoising methods based on spatial and spectral smoothing, facilitating the coral detection task.

Item URL in elib:https://elib.dlr.de/112905/
Document Type:Conference or Workshop Item (Speech)
Title:Unmixing-based Denoising as a pre-processing step for Coral Reef Analysis
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cerra, DanieleDaniele.Cerra (at) dlr.deUNSPECIFIED
Traganos, DimosthenisDimosthenis.Traganos (at) dlr.deUNSPECIFIED
Gege, Peterpeter.gege (at) dlr.dehttps://orcid.org/0000-0003-0939-5267
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:June 2017
Journal or Publication Title:International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.5194/isprs-archives-XLII-1-W1-279-2017
Page Range:pp. 279-282
Publisher:Copernicus Publications
Keywords:Hyperspectral remote sensing, Coral, Denoising, Water, Derivative Features, Spectral Unmixing
Event Title:ISPRS Hannover Workshop: HRIGI 17
Event Location:Hannover, Germany
Event Type:international Conference
Event Dates:06.-09.Juni 2017
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 > Photogrammetry and Image Analysis
Remote Sensing Technology Institute > Experimental Methods
Deposited On:30 Jun 2017 13:20
Last Modified:31 Jul 2019 20:10

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