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A spatial–spectral approach for deriving high signal qualityeigenvectors for remote sensing image transformations

Rogge, Derek and Bachmann, Martin and Rivard, Benoit and Aasbjerg Nielsen, Allan and Feng, Jilu (2014) A spatial–spectral approach for deriving high signal qualityeigenvectors for remote sensing image transformations. International Journal of Applied Earth Observation and Geoinformation, 26, pp. 387-398. Elsevier. doi: 10.1016/j.jag.2013.09.007. ISSN 0303-2434.

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Official URL: http://dx.doi.org/10.1016/j.jag.2013.09.007


Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformationof the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) canbe used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signalsources composing a given hyperspectral scene is generally much less than the number of spectral bands.Determining eigenvectors dominated by signal variance as opposed to noise is a difficult task. Problemsalso arise in using these transformations on large images, multiple flight-line surveys, or temporal datasets as computational burden becomes significant. In this paper we present a spatial–spectral approachto deriving high signal quality eigenvectors for image transformations which possess an inherently abil-ity to reduce the effects of noise. The approach applies a spatial and spectral subsampling to the data,which is accomplished by deriving a limited set of eigenvectors for spatially contiguous subsets. Thesesubset eigenvectors are compiled together to form a new noise reduced data set, which is subsequentlyused to derive a set of global orthogonal eigenvectors. Data from two hyperspectral surveys are used todemonstrate that the approach can significantly speed up eigenvector derivation, successfully be appliedto multiple flight-line surveys or multi-temporal data sets, derive a representative eigenvector set forthe full image data set, and lastly, improve the separation of those eigenvectors representing signal asopposed to noise.

Item URL in elib:https://elib.dlr.de/92957/
Document Type:Article
Title:A spatial–spectral approach for deriving high signal qualityeigenvectors for remote sensing image transformations
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Rogge, DerekGerman Remote Sensing Data CentreUNSPECIFIED
Bachmann, MartinUNSPECIFIEDhttps://orcid.org/0000-0001-8381-7662
Rivard, BenoitDepartment of Earth and Atmospheric Sciences, University of AlbertaUNSPECIFIED
Aasbjerg Nielsen, AllanTechnical University of Denmark, National Space InstituteUNSPECIFIED
Feng, JiluDepartment of Earth and Atmospheric Sciences, University of AlbertaUNSPECIFIED
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 387-398
Keywords:Hyperspectral imaging, Spatial and spectral processing, Eigenvector transformationsa
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben Fernerkundung der Landoberfläche (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Rogge, Derek
Deposited On:04 Dec 2014 14:37
Last Modified:26 Aug 2022 11:54

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