We have a DJI Phantom 4 RTK with Base Unit. I have flown several test jobs and didn’t have to many issues that I couldn’t figure out.
Now I have flown a real job (58 acres) and have a problem with the elevations of the GCP’s not matching my point cloud and contours as they are about 3 feet off but not consistent over the 5 GCP’s I have in place (point cloud and contours are higher elevation). I did the whole job in a couple hours (9am to 11am) with just battery changes.
It was windy that day and I almost didn’t fly it. I have 7 picture tie points per GCP. My Image input is WGS84 (meters) and my GCP’s and output are “NAD 83 Stateplane Texas North FIPS 4201 feet (deprecated) (EGM 96 Geoid)” which is how my test jobs were setup. The mosaic looks fine. Not sure why the problem of not fitting my GCP’s. I ran the whole (1-2-3) process over 2 times starting from scratch with the same answer (very long process time). Please could use some help.
I am including a quality report.
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Quality Report
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Summary
Project 8410 Loop 335
Processed 2020-12-08 10:32:53
Camera Model Name(s) FC6310R_8.8_5472x3648 (RGB)
Average Ground Sampling Distance (GSD) 3.58 cm / 1.41 in
Area Covered 0.933 km2 / 93.2699 ha / 0.36 sq. mi. / 230.5941 acres
Quality Check
Images median of 45276 keypoints per image
Dataset 1599 out of 1599 images calibrated (100%), all images enabled
Camera Optimization 0.9% relative difference between initial and optimized internal camera parameters
Matching median of 15991.3 matches per calibrated image
Georeferencing yes, 5 GCPs (5 3D), mean RMS error = 0.012 US survey foot
Preview
Figure 1: Orthomosaic and the corresponding sparse Digital Surface Model (DSM) before densification.
Calibration Details
Number of Calibrated Images 1599 out of 1599
Number of Geolocated Images 1599 out of 1599
Initial Image Positions
Figure 2: Top view of the initial image position. The green line follows the position of the images in time starting from the large blue dot.
Computed Image/GCPs/Manual Tie Points Positions
Uncertainty ellipses 1000x magnified
Figure 3: Offset between initial (blue dots) and computed (green dots) image positions as well as the offset between the GCPs initial positions (blue crosses) and
their computed positions (green crosses) in the top-view (XY plane), front-view (XZ plane), and side-view (YZ plane). Dark green ellipses indicate the absolute
position uncertainty of the bundle block adjustment result.
Absolute camera position and orientation uncertainties
X [US survey foot] Y [US survey foot] Z [US survey foot] Omega [degree] Phi [degree] Kappa [degree]
Mean 0.010 0.010 0.013 0.002 0.002 0.002
Sigma 0.000 0.001 0.001 0.000 0.000 0.000
Overlap
Number of overlapping images: 1 2 3 4 5+
Figure 4: Number of overlapping images computed for each pixel of the orthomosaic.
Red and yellow areas indicate low overlap for which poor results may be generated. Green areas indicate an overlap of over 5 images for every pixel. Good
quality results will be generated as long as the number of keypoint matches is also sufficient for these areas (see Figure 5 for keypoint matches).
Bundle Block Adjustment Details
Number of 2D Keypoint Observations for Bundle Block Adjustment 25364953
Number of 3D Points for Bundle Block Adjustment 9495565
Mean Reprojection Error [pixels] 0.085
Internal Camera Parameters
FC6310R_8.8_5472x3648 (RGB). Sensor Dimensions: 12.833 [mm] x 8.556 [mm]
EXIF ID: FC6310R_8.8_5472x3648
Focal
Length
Principal
Point x
Principal
Point y R1 R2 R3 T1 T2
Initial Values 3658.300 [pixel]
8.580 [mm]
2722.500 [pixel]
6.385 [mm]
1835.100 [pixel]
4.304 [mm] -0.269 0.112 -0.033 0.000 -0.001
Optimized Values 3691.322 [pixel]
8.657 [mm]
2697.647 [pixel]
6.327 [mm]
1807.494 [pixel]
4.239 [mm] -0.285 0.131 -0.040 0.000 -0.000
Uncertainties (Sigma) 0.023 [pixel]
0.000 [mm]
0.030 [pixel]
0.000 [mm]
0.032 [pixel]
0.000 [mm] 0.000 0.000 0.000 0.000 0.000
Correlated Independent
F
C0x
C0y
R1
R2
R3
T1
T2
The correlation between camera internal parameters
determined by the bundle adjustment. White indicates a full
correlation between the parameters, ie. any change in one can
be fully compensated by the other. Black indicates that the
parameter is completely independent, and is not affected by
other parameters.
The number of Automatic Tie Points (ATPs) per pixel, averaged over all images of the camera model,
is color coded between black and white. White indicates that, on average, more than 16 ATPs have
been extracted at the pixel location. Black indicates that, on average, 0 ATPs have been extracted at
the pixel location. Click on the image to the see the average direction and magnitude of the reprojection
error for each pixel. Note that the vectors are scaled for better visualization. The scale bar
indicates the magnitude of 1 pixel error.
2D Keypoints Table
Number of 2D Keypoints per Image Number of Matched 2D Keypoints per Image
Median 45276 15991
Min 20049 1268
Max 75691 34610
Mean 45109 15863
3D Points from 2D Keypoint Matches
Number of 3D Points Observed
In 2 Images 6063694
In 3 Images 1944429
In 4 Images 782972
In 5 Images 351376
In 6 Images 169325
In 7 Images 86583
In 8 Images 46768
In 9 Images 22389
In 10 Images 12214
In 11 Images 6780
In 12 Images 3821
In 13 Images 2250
In 14 Images 1226
In 15 Images 725
In 16 Images 367
In 17 Images 218
In 18 Images 169
In 19 Images 94
In 20 Images 58
In 21 Images 35
In 22 Images 27
In 23 Images 20
In 24 Images 11
In 25 Images 11
In 26 Images 2
In 27 Images 1
2D Keypoint Matches
Number of matches
25 222 444 666 888 1111 1333 1555 1777 2000
Figure 5: Computed image positions with links between matched images. The darkness of the links indicates the number of matched 2D keypoints between the
images. Bright links indicate weak links and require manual tie points or more images.
Geolocation Details
Ground Control Points
GCP Name Accuracy XY/Z [US
survey foot]
Error X [US
survey foot]
Error Y [US
survey foot]
Error Z [US
survey foot]
Projection Error
[pixel] Verified/Marked
0 (3D) 0.020/ 0.020 0.021 -0.004 -0.025 0.497 7 / 7
1 (3D) 0.020/ 0.020 0.000 0.006 0.018 0.436 7 / 7
2 (3D) 0.020/ 0.020 -0.005 0.005 -0.026 0.482 7 / 7
3 (3D) 0.020/ 0.020 -0.015 -0.013 0.006 0.688 7 / 7
4 (3D) 0.020/ 0.020 -0.001 0.000 0.005 0.423 7 / 7
Mean [US survey
foot] 0.000089 -0.001150 -0.004132
Sigma [US survey
foot] 0.011553 0.006678 0.017878
RMS Error [US
survey foot] 0.011553 0.006777 0.018349
Localisation accuracy per GCP and mean errors in the three coordinate directions. The last column counts the number of calibrated images where the GCP has
been automatically verified vs. manually marked.
Absolute Geolocation Variance
Min Error [US survey foot] Max Error [US survey foot] Geolocation Error X [%] Geolocation Error Y [%] Geolocation Error Z [%]
- -0.17 0.00 0.00 0.00
-0.17 -0.14 0.00 0.00 0.00
-0.14 -0.10 0.00 0.00 0.06
-0.10 -0.07 0.00 0.00 0.44
-0.07 -0.03 0.13 0.06 6.57
-0.03 0.00 48.66 51.09 45.03
0.00 0.03 51.22 48.78 41.03
0.03 0.07 0.00 0.06 6.38
0.07 0.10 0.00 0.00 0.44
0.10 0.14 0.00 0.00 0.06
0.14 0.17 0.00 0.00 0.00
0.17 - 0.00 0.00 0.00
Mean [US survey foot] -4.911925 1.415077 5.760546
Sigma [US survey foot] 0.008384 0.007674 0.023715
RMS Error [US survey foot] 4.911932 1.415098 5.760595
Min Error and Max Error represent geolocation error intervals between -1.5 and 1.5 times the maximum accuracy of all the images. Columns X, Y, Z show the
percentage of images with geolocation errors within the predefined error intervals. The geolocation error is the difference between the initial and computed image
positions. Note that the image geolocation errors do not correspond to the accuracy of the observed 3D points.
Geolocation Bias X Y Z
Translation [US survey foot] -4.911924 1.415058 5.761239
Bias between image initial and computed geolocation given in output coordinate system.
Relative Geolocation Variance
Relative Geolocation Error Images X [%] Images Y [%] Images Z [%]
[-1.00, 1.00] 99.87 99.94 99.44
[-2.00, 2.00] 100.00 100.00 100.00
[-3.00, 3.00] 100.00 100.00 100.00
Mean of Geolocation Accuracy [US survey foot] 0.037123 0.037123 0.073137
Sigma of Geolocation Accuracy [US survey foot] 0.001399 0.001399 0.005613
Images X, Y, Z represent the percentage of images with a relative geolocation error in X, Y, Z.
Geolocation Orientational Variance RMS [degree]
Omega 1.547
Phi 1.648
Kappa 3.357
Geolocation RMS error of the orientation angles given by the difference between the initial and computed image orientation angles.
Initial Processing Details
System Information
Hardware
CPU: Intel(R) Core™ i9-9900K CPU @ 3.60GHz
RAM: 64GB
GPU: NVIDIA GeForce GTX 1080 (Driver: 27.21.14.5167), Intel(R) UHD Graphics 630 (Driver: 26.20.100.7263)
Operating System Windows 10 Pro, 64-bit
Coordinate Systems
Image Coordinate System WGS 84
Ground Control Point (GCP) Coordinate System NAD_1983_StatePlane_Texas_North_FIPS_4201_Feet (deprecated) (EGM 96 Geoid)
Output Coordinate System NAD_1983_StatePlane_Texas_North_FIPS_4201_Feet (deprecated) (EGM 96 Geoid)
Processing Options
Detected Template No Template Available
Keypoints Image Scale Full, Image Scale: 1
Advanced: Matching Image Pairs Aerial Grid or Corridor
Advanced: Matching Strategy Use Geometrically Verified Matching: no
Advanced: Keypoint Extraction Targeted Number of Keypoints: Automatic
Advanced: Calibration
Calibration Method: Standard
Internal Parameters Optimization: All
External Parameters Optimization: All
Rematch: Auto, no
Point Cloud Densification details
Processing Options
Image Scale multiscale, 1/2 (Half image size, Default)
Point Density Optimal
Minimum Number of Matches 3
3D Textured Mesh Generation yes
3D Textured Mesh Settings: Resolution: Medium Resolution (default)
Color Balancing: no
LOD Generated: no
Advanced: 3D Textured Mesh Settings Sample Density Divider: 1
Advanced: Image Groups group1
Advanced: Use Processing Area yes
Advanced: Use Annotations yes
Results
Number of Generated Tiles 9
Number of 3D Densified Points 137551877
Average Density (per US survey foot3) 3.03
DSM, Orthomosaic and Index Details
Processing Options
DSM and Orthomosaic Resolution 1 x GSD (3.58 [cm/pixel])
DSM Filters Noise Filtering: yes
Surface Smoothing: yes, Type: Sharp
Raster DSM
Generated: yes
Method: Inverse Distance Weighting
Merge Tiles: yes
Orthomosaic
Generated: yes
Merge Tiles: yes
GeoTIFF Without Transparency: no
Google Maps Tiles and KML: yes
Grid DSM Generated: yes, Spacing [cm]: 100
Raster DTM Generated: yes
Merge Tiles: yes
DTM Resolution 5 x GSD (3.58 [cm/pixel])
Contour Lines Generation
Generated: yes
Contour Base [US survey foot]: 0
Elevation Interval [US survey foot]: 1
Resolution [cm]: 100
Minimum Line Size [vertices]: 20
