View source: R/JPLLK_surface.r

JPLLK_surface | R Documentation |

Estimate surface using piecewise local linear kernel smoothing. The bandwidth is chosen by leave-one-out cross validation.

```
JPLLK_surface(image, bandwidth, plot = FALSE)
```

`image` |
A square matrix, no missing value allowed. |

`bandwidth` |
A numeric vector of positive integers, which specifies the number of pixels used in the local smoothing. The final fitted surface uses the optimal bandwidth chosen 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. (2009) "Jump-Preserving Surface Reconstruction from Noisy Data", *Annals of the Institute of Statistical Mathematics*, **61**(3), 715 – 751, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10463-007-0166-9")}.

`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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