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/10754/620997
Title:
Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model
Authors:
Angel, Yoseline ( 0000-0002-8377-8736 ) ; 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 multi-temporal 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:
Division of Biological and Environmental Sciences and Engineering; Water Desalination & Reuse Research Cntr
Conference/Event name:
SPIE Remote Sensing
Issue Date:
26-Sep-2016
Type:
Poster
Appears in Collections:
Posters

Full metadata record

DC FieldValue Language
dc.contributor.authorAngel, Yoselineen
dc.contributor.authorHouborg, Rasmusen
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2016-10-13T12:16:46Z-
dc.date.available2016-10-13T12:16:46Z-
dc.date.issued2016-09-26-
dc.identifier.urihttp://hdl.handle.net/10754/620997-
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 multi-temporal 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.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjecthyperspectralen
dc.subjectmultitemporalen
dc.subjectnonseparableen
dc.subjectCovariance modelsen
dc.subjectSpatio-temporal dataen
dc.subjectKrigingen
dc.subjectremote sensingen
dc.titleRemoval of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance modelen
dc.typePosteren
dc.contributor.departmentDivision of Biological and Environmental Sciences and Engineeringen
dc.contributor.departmentWater Desalination & Reuse Research Cntren
dc.conference.nameSPIE Remote Sensingen
dc.conference.locationEdinburgh, Scotlanden
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