The list of supported sensors in PCI Geomatica can be found here: http://www.pcigeomatics.com/resources/geomatica/sensor-support
The list of supported sensors in GXL can be found here: http://www.pcigeomatics.com/pdf/GXL2016-SensorList.pdf
Residuals are the difference between where the GCP has been placed, and where the bundle adjustment computes the position of that point to be. The bundle adjustment solves for the best possible solution for the location of each image, using all GCPs, TPs, and EO (exterior orientation) data available.
The criterion for this solution is that the sum of the square errors is minimized (least squares), which means that no single GCP or TP will fit perfectly. The residuals are the remaining shift in the computed position. Residuals are not errors to be corrected, they simply help you to see which GCPs fit well (low residuals) and which don't fit quite as well (higher residuals). High residuals can tell you if a single GCP is severely out of place, or more often, where parts of the block of imagery do not fit the ground well.
Normally, you aim for residuals of less than 1 pixel. This assumes that you have the ability to measure the GCPs to that accuracy on the imagery. However, if your GCPs are not accurate to 1 image pixel (often the case for high resolution imagery), then it is unlikely that you will achieve that goal. If you are using GPS/INS, you will be limited to the accuracy of that data. Also, if you are using a DEM for GCP elevations, this can introduce error. So, the fact that you don't achieve residuals under 1 pixel does not necessarily mean you do not have the best possible solution. The magnitude of the residuals should reflect the accuracy of all data sources, as well as the image resolution.
Data snooping refers to standardized residuals, which are the residual values divided by the RMS error. This allows you to tell if your residuals are abnormally high, or just appear high because the overall accuracy is low. For example, you may have residuals of 10m. You find, however, that your GCPs are only accurate to 20m. This produces a high RMS error for the block. When the residuals are normalized, you may find values such as 0.002, indicating that the residual is probably OK for the variability of that data. I find that data snooping is valuable in cases where you are trying to determine how accurate your GCP or EO data is.
This is caused by errors in your DEM. For a side-looking sensors, pixel displacement (parallax) due to terrain elevation is mostly in the along the line direction. Consequently, the errors in DEM have much stronger effect on the error in X direction. A 3:1 ratio between X and Y errors seems quite reasonable, particularly for a satellite sensor with good attitude control.
There are a number of areas within OrthoEngine where units could be represented in feet or metres. You should carefully consider the units during the course of your project.
- Units for the elevation of the input GCP's
- Units for the elevation of the input DEM. May be the same as GCP's or may not be.
- Units for the Output projection (i.e. FOOT or SPAF)
- Units for the GCP projection (i.e. FOOT or SPAF)
How this fits into your OrthoEngine workflow:
- Options > Elevation Units: This is set based on the elevation of the GCPs imported from a text file, or the altitude as given in an exterior orientation file. If you are extracting GCP elevations from your DEM, then use the same units of elevation as your DEM. Default is meter.
- DEM units for orthorectification. If the elevation units of your DEM do not match the units of the Output Projection, then the "Elevation Scale" setting (under DEM options in the Ortho Generation panel) must be set to scale the data to the correct units. Alternatively, the "Utilities" > "Replace Image Values" tool can be used to transform the elevations to the correct units. For example,if the output projection is in a foot-based projection (FOOT or SPAF) then the DEM elevation units must be in feet. If the output projection is a metre based projection (UTM, SPCS) then the DEM elevation units must be in metre (feet>metre multiply 0.3048, metre>feet divide 0.3048)
Version 10 provides an option within the Ortho Generation panel to select the appropriate units so that elevations of either feet or meters can be used with any output project projection.
- Automatic Tie Point Average Elevation: The average elevation should be set based on the GCP Elevation Units, set under the Options menu.
- DEM Generation Elevation Range: These values should be set based on the units of the output projection. (i.e. feet for FOOT or SPAF, metres for SPCS or UTM).
Minimum number of GCPs under ideal conditions:
|ASAR (Envisat - 1B)||8|
|ASTER (1A or 1B)||6|
|IKONOS Geo Product||6|
|MERIS (Envisat - 1B)||6|
|RADARSAT Specific Model||0|
|RADARSAT Toutin's Model||8|
|SPOT 1, 2, 3||4|
|SPOT 5 (1A)||6|
|QuickBird (Ortho Ready Standard)||8|
- Rational Functions Computed from GCPs: 5 per image (19 per image is recommended)
- Rational Functions Extracted from ImageFile: None required (accuracy is improved with 1 or more GCPs).
Matching Threshold is the minimum correlation score that will be considered to be a valid match. Setting the matching threshold adjusts the quality statistic so that only the best correlations are considered successful matches, or so that less successful correlations are accepted. This will not affect the speed, but rather the total number of points that you get.
For example, if you have really clear, high-resolution data, you may be able to set your matching threshold very high, to 0.85 or better. This will make sure that you get very highly correlated points, and won't accept any questionable matches. However, if you are dealing with lower resolution, or fuzzier data, you are likely to get very few successful matches with a threshold of 0.75 or better. In this case, you would change it to a lower value such as 0.6 in order to accept more points.
Rational Functions (RF) are commonly used in satellite imagery. It is a way to supply the ortho transformation coefficients without releasing the sensor information. It is an approximation of a rigorous model used by NIMA for their NITF format, Space Imaging for their IKONOS data and Digital Globe for QuickBird imagery RF are better than standard polynomials because it supports elevation as well. Thin Plate Spline (TPS) requires many more GCPs than RF. For example, 20 coefficients require only 40 GCPs for RF. TPS is like an exact fit (each point has a very small area of influence) and it works well if you have many GCPs.
The output DEM will often contain black squares in your DEM, these features will usually contain your NODATA value and indicate that there was trouble interpolating the elevation for that pixel. If the holes are relatively small then you can use the DEM editing tools to generate a mask of the NODATA pixels and interpolate a DN value based on surrounding pixels.
Yes, Geomatica has DEM editing tools that can be used to edit existing or the DEMs that result from the interpolation process. You can also edit either the resultant geocoded DEM or edit the Epipolar DEMs that are used to produce the final raster surface prior to the interpolation surface. If you want to edit the DEM before it is geocoded, do not select Create Geocoded DEM option.
The DEM extraction will produce a file that contains the epipolar pair in the first channel, the correlation score (if selected) in the second channel, and the corresponding epipolar DEM in the third channel. You can also edit the geocoded DEM after it is generated, however, the file will not include the epipolar pairs. If you selected Create Score Channel, the correlation score is saved as the first channel in the file and the geocoded DEM as the second one. You can then open the DEM editing tools window and edit the DEM from there.
There are a few options available for this such as you could use Automatic Mosaicking to combine the DEMs, However using DEM from raster file tool is designed specifically for this purpose. (found under the Import and Build DEM processing step). This tool will allow you to import several raster DEM surfaces and then create one seamless (assuming that there is overlap between them) DEM output from them.