Adding GCP's to Pix4D Software

Adding GCP's to Pix4D Software

Pix4D is an imaging processing software and can convert large image sets into georeferenced 2D mosaics and 3D models by constructing point clouds. Pix4D has a wide range of applications including precision agriculture, mines and quarries mapping, natural resource management, emergency response, construction, archaeology and more. The software is extremely user friendly - almost to a fault as users can easily neglect to adjust coordinate systems for X, Y, and Z values.

Ground Control Points (GCP) are ground coordinates used to correctly position imagery in relation to the Earth. GCP’s offers an excellent mean to improve quality of aerial imagery acquisition and data set accuracy. GCP’s are useful tool in the field but it is crucial that they are collected in an appropriate way that yields accurate, high quality results. GCP’s are collected by using a survey grade global positioning system (GPS) to collect X, Y, and Z coordinates via triangulation. Pix4D software has the capability to use these coordinates but as mentioned before it can be easy to neglect picking an appropriate coordinate system to help reduce distortion. 

The goal of this lab is to familiarize ourselves with using GCPs to create an orthomosaic in Pix4D software. Following the introduction to Pix4D last week, we take a step further from georeferenced images and use the GCPs collected from the field to tie down the images. While Pix4D can produce images without GCPs, as shown last week, they are able to enhance the accuracy of the product. Accuracy will vary depending on type of GPS used to collect points. The Pix4D manual highlights three methods to add GCPs. Method A (Figure 1) is used when the image geolocation and the GCPs have a known coordinate system.This is the most common method and requires the least amount of time and less manual input as other methods.

                                                      Figure 1: Method A

Method B (Figure 2) is used when the initial images are without geolocation, the initial images are geolocated in a local coordinate system, or the GCPs are in a local coordinate system.

                                                    Figure 2: Method B

Method C works for any case, no matter the coordinate system of the images or GCPs. Method C (Figure 3) is the most time consuming as the GCPs require more manual intervention and is the best choice for "over night processing" (Method A and B are not).

Figure 3: Method C    

Methods

Here I will elaborate on the methods used to add GCPs and process the imagery. As mentioned before Method A was used to add GCPs because the coordinate systems were known and could be selected from Pix4Dmapper's database. Because Pix4D is user friendly the two coordinate systems do not need to be the same as Pix4D is able to convert between them. This is the most common method of adding GCPs.  It allows to mark the GCPs on the image with minimum manual intervention. But before we can do that we must first create a new project and add images as done in the previous blog. There were a total of 312 images from the flight collected by a Sony ILCE-6000 with geosnap, with a default coordinate system of WGS84 which was changed to NAD83 UTM Zone 15N to limit distortion in our small area of interest. The vertical coordinate system was set to mean sea level (MSL) egm96 to account for vertical distortion usually present with TopCon coordinate system.

Once data has been correctly added and the initial processing complete GCP's can be imported using the GCP/Manual Tie Point Manager. Make sure the GCP coordinate system is correct. The GCP's should be visible as blue X's (Figure 4). From here the GCP/Manual Tie Point manager can be opened to adjust and correct the imagery (Figure 5). At a minimum three images must be corrected, but in this project roughly 6 - 9 GCPs were corrected.

Figure 4: The blue x's indicate the location of the GCPs.

Figure 5: Here is a portion of the Manual Tie Point Manager Window
where GCPs were corrected. The more zoomed in an image is the more weight
it carries in correcting the rest of the images with this GCP.

After the GCP's have been corrected the final processing can be complete. A report will also be generated. After this the same imagery was processed without GCP's to investigate the differences.

Results

After final processing there are three important products: and RGB mosaic, a DSM, and a quality report. Figure 5 was made in ArcScene with 25x25 meter grids.

To further explore the data results the quality report was examined. Imagery processed with out GCPs  appears to be given a lesser RMS value (Figure 7) than the imagery processed with GCPs (Figure 8). This seems counterintuitive because with GCP's the mosiac image should become more accurate and have less error.

Conclusion

While the RMS error scores can be confusing - collecting GCP's and using them to tie down your imagery is one of the best techniques to improve accuracy. While GeoSnap claims high accuracy there seem to be some discrepancy in regards to z axis or vertical accuracy. In light of this though, collecting GCP's isn't always viable in some cases. GCP's can take a while to set up and collect their location. GeoSnap is useful for areas where setting up and collecting GCP's would be difficult or unnecessary. While using GCP's is the the most accurate if using surveyor grade equipment, I believe using the GeoSnsap will be good enough in some cases.