Provided by: grass-doc_6.4.3-3_all 
NAME
v.surf.bspline - Bicubic or bilinear spline interpolation with Tykhonov regularization.
KEYWORDS
vector, interpolation
SYNOPSIS
v.surf.bspline
v.surf.bspline help
v.surf.bspline [-ce] input=name [sparse=name] [output=name] [raster=name]
[sie=float] [sin=float] [method=string] [lambda_i=float] [layer=integer]
[column=name] [--overwrite] [--verbose] [--quiet]
Flags:
-c
Find the best Tykhonov regularizing parameter using a "leave-one-out" cross validation
method
-e
Estimate point density and distance
Estimate point density and distance for the input vector points within the current
region extends and quit
--overwrite
Allow output files to overwrite existing files
--verbose
Verbose module output
--quiet
Quiet module output
Parameters:
input=name
Name of input vector map
sparse=name
Name of input vector map of sparse points
output=name
Name for output vector map
raster=name
Name for output raster map
sie=float
Length of each spline step in the east-west direction
Default: 4
sin=float
Length of each spline step in the north-south direction
Default: 4
method=string
Spline interpolation algorithm
Options: bilinear,bicubic
Default: bilinear
lambda_i=float
Tykhonov regularization parameter (affects smoothing)
Default: 0.01
layer=integer
Layer number
If set to 0, z coordinates are used. (3D vector only)
Default: 0
column=name
Attribute table column with values to interpolate (if layer>0)
DESCRIPTION
v.surf.bspline performs a bilinear/bicubic spline interpolation with Tykhonov
regularization. The input is a 2D or 3D vector points map. Values to interpolate can be
the z values of 3D points or the values in a user-specified attribue column in a 2D or 3D
map. Output can be a raster or vector map. Optionally, a "sparse point" vector map can be
input which indicates the location of output vector points.
From a theoretical perspective, the interpolating procedure takes place in two parts: the
first is an estimate of the linear coefficients of a spline function is derived from the
observation points using a least squares regression; the second is the computation of the
interpolated surface (or interpolated vector points). As used here, the splines are 2D
piece-wise non-zero polynomial functions calculated within a limited, 2D area. The length
of each spline step is defined by sie for the east-west direction and sin for the north-
south direction. For optimum performance, the length of spline step should be no less than
the distance between observation points. Each vector point observation is modeled as a
linear function of the non-zero splines in the area around the observation. The least
squares regression predicts the the coefficients of these linear functions.
Regularization, avoids the need to have one one observation and one coefficient for each
spline (in order to avoid instability).
With regularly distributed data points, a spline step corresponding to the maximum
distance between two points in both the east and north directions is sufficient. But often
data points are not regularly distributed and require statistial regularization or
estimation. In such cases, v.surf.bspline will attempt to minimize the gradient of
bilinear splines or the curvature of bicubic splines in areas lacking point observations.
As a general rule, spline step length should be greater than the mean distance between
observation points (twice the distance between points is a good starting point). Separate
east-west and north-south spline step length arguments allows the user to account for some
degree of anisotropy in the distribution of observation points. Short spline step
lengths--especially spline step lengths that are less than the distance between
observation points--can greatly increase processing time.
Moreover, the maximum number of splines for each direction at each time is fixed,
regardless of the spline step length. As the total number of splines used increases (i.e.,
with small spline step lengths), the region is automatically into subregions for
interpolation. Each subregion can contain no more than 150x150 splines. To avoid subregion
boundary problems, subregions are created to partially overlap each other. A weighted mean
of observations, based on point locations, is calculated within each subregion.
The Tykhonov regularization parameter ("lambda_i") acts to smooth the interpolation. With
a small lambda_i, the interpolated surface closely follows observation points; a larger
value will produce a smoother interpolation.
The input can be a 2D pr 3D vector points map. If "layer =" 0 the z-value of a 3D map is
used for interpolation. If layer > 0, the user must specify an attribute column to used
for interpolation using the "column=" argument (2D or 3D map).
v.surf.bspline can produce a raster OR a vector output (NOT simultaneously). However, a
vector output cannot be obtained using the default GRASS DBF driver.
If output is a vector points map and a "sparse=" vector points map is not specified, the
output vector map will contain points at the same locations as observation points in the
input map, but the values of the output points are interpolated values. If instead a
"sparse=" vector points map is specified, the output vector map will contain points at the
same locations as the sparse vector map points, and values will be those of the
interpolated raster surface at those points.
A cross validation "leave-one-out" analysis is available to help to determine the optimal
lambda_i value that produces an interpolation that best fits the original observation
data. The more points used for cross-validation, the longer the time needed for
computation. Empirical testing indicates a threshold of a maximum of 100 points is
recommended. Note that cross validation can run very slowly if more than 100 observations
are used. The cross-validation output reports mean and rms of the residuals from the true
point value and the estimated from the interpolation for a fixed series of lambda_i
values. No vector nor raster output will be created when cross-validation is selected.
EXAMPLES
Basic interpolation
v.surf.bspline input=point_vector output=interpolate_surface method=bicubic
A bicubic spline interpolation will be done and a vector points map with estimated (i.e.,
interpolated) values will be created.
Basic interpolation and raster output with a longer spline step
v.surf.bspline input=point_vector raster=interpolate_surface sie=25 sin=25
A bilinear spline interpolation will be done with a spline step length of 25 map units.
An interpolated raster map will be created at the current region resolution.
Estimation of lambda_i parameter with a cross validation proccess
v.surf.bspline -c input=point_vector
Estimation on sparse points
v.surf.bspline input=point_vector sparse=sparse_points output=interpolate_surface
An output map of vector points will be created, corresponding to the sparse vector map,
with interpolated values.
Using attribute values instead Z-coordinates
v.surf.bspline input=point_vector raster=interpolate_surface layer=1 column=attrib_column
The interpolation will be done using the values in attrib_column, in the table associated
with layer 1.
BUGS
Known issues:
In order to avoid RAM memory problems, an auxiliary table is needed for recording some
intermediate calculations. This requires the "GROUP BY" SQL function is used, which is not
supported by the "dbf" driver. For this reason, vector map output "output=" is not
permitted with the DBF driver. There are no problems with the raster map output from the
DBF driver.
SEE ALSO
v.surf.idw, v.surf.rst
AUTHORS
Original version in GRASS 5.4: (s.bspline.reg)
Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni, Mirko Reguzzoni
Update for GRASS 6.X and improvements:
Roberto Antolin
REFERENCES
Brovelli M. A., Cannata M., and Longoni U.M., 2004, LIDAR Data Filtering and DTM
Interpolation Within GRASS, Transactions in GIS, April 2004, vol. 8, iss. 2, pp.
155-174(20), Blackwell Publishing Ltd
Brovelli M. A. and Cannata M., 2004, Digital Terrain model reconstruction in urban areas
from airborne laser scanning data: the method and an example for Pavia (Northern Italy).
Computers and Geosciences 30, pp.325-331
Brovelli M. A e Longoni U.M., 2003, Software per il filtraggio di dati LIDAR, Rivista
dell'Agenzia del Territorio, n. 3-2003, pp. 11-22 (ISSN 1593-2192)
Antolin R. and Brovelli M.A., 2007, LiDAR data Filtering with GRASS GIS for the
Determination of Digital Terrain Models. Proceedings of Jornadas de SIG Libre, Girona,
España. CD ISBN: 978-84-690-3886-9
Last changed: $Date: 2012-12-27 09:22:59 -0800 (Thu, 27 Dec 2012) $
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