Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

Handle URI:
http://hdl.handle.net/10754/623307
Title:
Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model
Authors:
Angel, Yoseline; Houborg, Rasmus; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multitemporal data and spatio-Temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-Temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Water Desalination and Reuse Research Center (WDRC)
Citation:
Angel Y, Houborg R, McCabe MF (2016) Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model . Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII. Available: http://dx.doi.org/10.1117/12.2241518.
Publisher:
SPIE-Intl Soc Optical Eng
Journal:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Conference/Event name:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Issue Date:
25-Oct-2016
DOI:
10.1117/12.2241518
Type:
Conference Paper
Additional Links:
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2577863
Appears in Collections:
Conference Papers; Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAngel, Yoselineen
dc.contributor.authorHouborg, Rasmusen
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2017-05-01T12:23:23Z-
dc.date.available2017-05-01T12:23:23Z-
dc.date.issued2016-10-25en
dc.identifier.citationAngel Y, Houborg R, McCabe MF (2016) Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model . Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII. Available: http://dx.doi.org/10.1117/12.2241518.en
dc.identifier.doi10.1117/12.2241518en
dc.identifier.urihttp://hdl.handle.net/10754/623307-
dc.description.abstractHyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multitemporal data and spatio-Temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-Temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.en
dc.publisherSPIE-Intl Soc Optical Engen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2577863en
dc.rightsCopyright 2016 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en
dc.subjectCovarianceen
dc.subjectHyperspectralen
dc.subjectKrigingen
dc.subjectMulti-Temporalen
dc.subjectNon-separableen
dc.subjectR softwareen
dc.subjectRemote sensingen
dc.subjectSpatio-Temporalen
dc.titleRemoval of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance modelen
dc.typeConference Paperen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.identifier.journalRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIIIen
dc.conference.date2016-09-26 to 2016-09-28en
dc.conference.nameRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIIIen
dc.conference.locationEdinburgh, GBRen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorAngel, Yoselineen
kaust.authorHouborg, Rasmusen
kaust.authorMcCabe, Matthewen
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