Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model
dc.contributor.author | Angel, Yoseline | |
dc.contributor.author | Houborg, Rasmus | |
dc.contributor.author | McCabe, Matthew | |
dc.date.accessioned | 2017-05-01T12:23:23Z | |
dc.date.available | 2017-05-01T12:23:23Z | |
dc.date.issued | 2016-10-25 | |
dc.identifier.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. | |
dc.identifier.doi | 10.1117/12.2241518 | |
dc.identifier.uri | http://hdl.handle.net/10754/623307 | |
dc.description.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. | |
dc.publisher | SPIE-Intl Soc Optical Eng | |
dc.relation.url | http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2577863 | |
dc.rights | Copyright 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.subject | Covariance | |
dc.subject | Hyperspectral | |
dc.subject | Kriging | |
dc.subject | Multi-Temporal | |
dc.subject | Non-separable | |
dc.subject | R software | |
dc.subject | Remote sensing | |
dc.subject | Spatio-Temporal | |
dc.title | Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model | |
dc.type | Conference Paper | |
dc.contributor.department | Biological and Environmental Sciences and Engineering (BESE) Division | |
dc.contributor.department | Environmental Science and Engineering Program | |
dc.contributor.department | Water Desalination and Reuse Research Center (WDRC) | |
dc.identifier.journal | Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII | |
dc.conference.date | 2016-09-26 to 2016-09-28 | |
dc.conference.name | Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII | |
dc.conference.location | Edinburgh, GBR | |
dc.eprint.version | Publisher's Version/PDF | |
kaust.person | Angel Lopez, Yoseline | |
kaust.person | Houborg, Rasmus | |
kaust.person | McCabe, Matthew | |
refterms.dateFOA | 2018-06-13T22:22:01Z |
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Environmental Science and Engineering Program
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Water Desalination and Reuse Research Center (WDRC)