Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Water Desalination and Reuse Research Center (WDRC)
Permanent link to this recordhttp://hdl.handle.net/10754/623307
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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.
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.
PublisherSPIE-Intl Soc Optical Eng
Conference/Event nameRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIII