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dc.contributor.authorAngel, Yoseline
dc.contributor.authorHouborg, Rasmus
dc.contributor.authorMcCabe, Matthew
dc.date.accessioned2017-05-01T12:23:23Z
dc.date.available2017-05-01T12:23:23Z
dc.date.issued2016-10-25
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.
dc.identifier.doi10.1117/12.2241518
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.
dc.publisherSPIE-Intl Soc Optical Eng
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2577863
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.
dc.subjectCovariance
dc.subjectHyperspectral
dc.subjectKriging
dc.subjectMulti-Temporal
dc.subjectNon-separable
dc.subjectR software
dc.subjectRemote sensing
dc.subjectSpatio-Temporal
dc.titleRemoval of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model
dc.typeConference Paper
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.identifier.journalRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
dc.conference.date2016-09-26 to 2016-09-28
dc.conference.nameRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
dc.conference.locationEdinburgh, GBR
dc.eprint.versionPublisher's Version/PDF
kaust.personAngel Lopez, Yoseline
kaust.personHouborg, Rasmus
kaust.personMcCabe, Matthew
refterms.dateFOA2018-06-13T22:22:01Z


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