View source: R/JPLLK_surface.r
JPLLK_surface | R Documentation |
Estimate surface using piecewise local linear kernel smoothing. Bandwidth is chosen by leave-one-out cross validation.
JPLLK_surface(image, bandwidth, plot = FALSE)
image |
A square matrix object of size n by n, no missing value allowed. |
bandwidth |
A numeric vector with positive integers, which specify the number of pixels used in the local smoothing. The final fitted surface chooses the optimal bandwidth from those provided by users. |
plot |
If plot = TRUE, the image of the fitted surface is plotted |
At each pixel, the gradient is estimated by a local linear kernel smoothing procedure. Next, the local neighborhood is divided into two halves along the direction perpendicular to (\widehat{f}'_{x}, \widehat{f}'_{y}). Then the one- sided local linear kernel (LLK) estimates are obtained in the two half neighborhoods respectively. Among these two one-sided estimates, the one with smaller weighted mean square error is chosen to be the final estimate of the regression surface at the pixel.
A list of fitted values, residuals, chosen bandwidth and estimated sigma.
Qiu, P., "Jump-preserving surface reconstruction from noisy data", Annals of the Institute of Statistical Mathematics, 61(3), 2009, 715-751.
threeStage
, surfaceCluster
data(sar) # SAR image is bundled with the package and it is a # standard test image in statistics literature. fit = JPLLK_surface(image=sar, bandwidth=c(3, 4))
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