Using Geomatica Software - Fernerkundung Jena - Friedrich ...

slow: on a Sun SPARCstation LX the evaluation rate is 1440 pixels per second for a
model with 83 GCPs. The other disadvantage of TPSs is that they reproduce exactly
(within numerical computation errors) the values at all GCPs. Therefore the method does
not provide direct means of detecting and correcting errors in GCP coordinates. To verify
the derived transformation, an independent set of Check Points must be acquired and
their number should be large enough to ensure a thorough verification (say, half the
number of GCPs). This obviously increases the total cost of the approach, or may
compromise the quality of the final product if the number of check points is low. To
summarize, Thin Plate Spline warping is recommended for distortions that can be
accurately represented by up to several tens of ground control points. It is not
recommended for removal of terrain distortion, for which an analytical approach should
be used. The analytical approach is based on a photogrammetric model of the viewing
geometry and uses terrain elevations from a Digital Elevation Model of the area.
Satellite Ortho Model:
The Satellite Ortho Model is the geometric model used in the PCI PACE Satellite Ortho
and DEM package. In GCPWorks, it is only enabled when the user selects the Satellite
Ortho Correction Mathematical Model from the GCPWorks Setup panel. It should be
noted that the residual errors reported in GCPWorks are in PIXEL unit on the raw image,
assuming the input geocoded coordinates are correct. This is equivalent to setting
ERRUNIT=PIXEL inside the SMODEL program of the satellite ortho package. The
polynomial transformation method, in comparison, does not reflect the distortions due to
the image acquisition and relief displacements. The polynomial transformation method is
limited to small areas with at terrain and requires a lot of GCPs. The PCI satellite ortho
model only requires a minimum of three to four accurate GCPs for visible images (such
as SPOT and Landsat) and seven accurate GCPs for RADAR images (such as ERS and
RADARSAT). The geometric modelling was developed by Dr. Thierry Toutin, at the
Canada Centre for Remote Sensing. The model requires a minimum of 4 and 7 GCPs for
VIR (such as Landsat, SPOT, IRS) and SAR images, respectively. However, if the GCPs
are not very accurate, the user should collect at least 6 and 12 GCPs for VIR and SAR
images, respectively. More details on satellite ortho model can be found in the
Registration Scenarios section. Also see the Satellite Ortho & DEM manual, and the
Orthorectification and DEM Extraction chapter of the Using PCI Software manual.
Resampling:
Resampling is a process that involves the extraction and interpolation of grey levels from
pixel locations in the original uncorrected image. There are several methods of
interpolation which can be applied: nearest neighbor, bilinear interpolation, cubic
convolution, 8-point Sin(x)/x, and 16-point Sin(x)/x. During bilinear interpolation, cubic
convolution and sin(x)/x, the last pixel and/or scanline will be reused to produce the grey
level values for the georeferenced image if the uncorrected pixel window used to
calculate the corrected matrix grey level value falls outside the bounds of the
uncorrected image at its corner edges. This reuse of pixel values can have the effect of
producing image edges which are darker or lighter than the interior of the image. In
GCPWorks, the Resampling type can be set in the Disk-to-Disk-Registration, or the Preregistration
Checking panel.
Nearest Neighbor:
Nearest neighbor interpolation determines the grey level from the closest pixel to the
specified input coordinates, and assigns that value to the output coordinates. This
method is considered the most efficient in terms of computation time. Because it does
not alter the grey level value, a nearest neighbor interpolation is preferred if subtle
variations in the grey levels need to be retained, if classification will follow the
registration, or if a classified image is to be resampled. Nearest neighbor interpolation
introduces a small error into the newly registered image. The image may be offset