1. Automatically Collect Ground Control Points from Online Aerial Maps
The 36th Asian Conference on Remote Sensing
Quezon City, Metro Manila Philippines
Automatically Collect Ground Control Points from Online Aerial Maps
Tengfei Long
Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences
October 22, 2015

2. Outline
Background
Technical frame
Online matching
Results
Conclusions

3. Background
Ground Control Points (GCPs) for remotely sensed (RS) images
Necessity
Satellite
90% CE (meters)
GeoEye-1
2.5
WorldView1
7.6
WorldView2
12.2
Quickbird 23
Variable errors in exterior-orientation parameters, and most of remotely sensed images are not ready to use
Geometric bias
(Ozcanli et al )
Difficulties
Scene source
Size (Pixel×Pixel)
GF-1 WFV
12000 ×13400
GF-1 PAN
18192 ×18000
GF-2 PAN
29200 ×27619
Large data volume
Multi-source: different time, different sensor, different spectral
Automatic approach
High resolution
Large data volume
Expensive
Reference images

4. Better than 5 meters in many study areas.
Background
Reference images  Online resources
Google satellite images (Google Earth)
Bing aerial images
MapQuest satellite maps
Mapbox satellite images …
Better than 5 meters in many study areas.
Free or cheap, and geometric accuracy is improving
But…
No automatic solution

5. Area-based matching (ABM) Feature-based matching (FBM)
Background
Area-based matching (ABM)
Feature-based matching (FBM)
Automatic registration
Normalized cross-correlation
Least squares matching
Phase correlation
Mutual Information
SIFT, SURF, FAST, …
Iterative Closest Point
Soft assignment
Probabilistic methods
SIFT
Most robust and widely used
Computational expensive
Sensitive to noise, illumination and geometric distortion
Accurate
Efficient
Robust to geometric distortion, illumination and occlusion
Less accurate

6. Problems Challenges when SIFT directly used in RS images：
Large image size: time consuming
Large scene: similar features, less distinct
Accuracy: extracted independently
Distribution: not well distributed
Outliers: complicate distortion

7. Outline
Background
Technical frame
Online matching
Results
Conclusions

8. Analysis
Considering the specific task of geometric rectification of remotely sensed image
Initial imaging models are available
RPCs, rigorous sensor model, affine transformation in Geo-referenced images, etc.
Only tens of GCPs are required
Too many GCPs do not necessarily benefit accuracy, and tens of GCPs are enough.
Distribution of GCPs is important

9. Improvement strategies
Image tiling
Small image tile (256 × 256) and simple distortion
Small scene
Well distributed
Using prior geometric information
Predict master image tile
Using attributes of SIFT feature
Scale, contrast, dominant orientation
Refining the results of SIFT
Least squares matching, sub-pixel accuracy

10. Least squares matching
Technical frame
Technical frame based on image tiling and improved SIFT
Initial imaging model
Slave image Tile
Master image Tile
Master image(s)
Cross matching
Distance ratio
SIFT
matching
Scale constraints
Angle constraints
Similarity transformation
Affine transformation
Outlier removing
Least squares matching
Slave image tiling
Largest contrast

11. Outline
Background
Technical frame
Online matching
Results
Conclusions

12. Online Aerial Maps Many Providers Unified Projection Multiple Scales
Google, Bing, MapBox, MapQuest, ESRI, Yandex…
Unified Projection
Web Mercator projection
Multiple Scales
Zoom 1~Zoom 23
Global reference
250m ~ 0.3m

13. Static Maps API Service
Download online reference image tile via Static Maps API Service

14. Online image tile parameters
Calculate zoom level from ground sample distance (GSD) i.e. spatial resolution
Calculate online image coordinates from longitude (λ) and latitude (φ)
width and height
Calculate longitude (λ) and latitude (φ) from online image coordinates

15. Online Matching
A slave image tile matched with different online aerial tiles

16. Outline
Background
Technical frame
Online matching
Results
Conclusions

17. Robustness
Compare with ordinary SIFT (Lowe 2004) and SR-SIFT (Yi et al. 2008)
12 test tiles from 6 image pairs including Landsat-5 vs Landsat-5 , HJ-1 vs Landsat-8, GF-1 vs Bing aerial map, ZY-3 vs RapidEye, GF-1 MSS vs GF-1 PAN, Kompsat-2 vs Worldview-1
Tiles
Proposed
method
Ordinary
SIFT
SR-SIFT 1 17
14 (11) 2 5
6 (4)
7 (6) 3 9
8 (6) 4 11
13 (9) 8
10 (5) 6 28
49 (26)
50 (25) 7
16 (5)
17 (6)
34 (3)
105
104 (102)
103 (101) 10
101
102 (98)
0 (0) 12
4 (2)
5 (3)

18. Least Squares Matching
Accuracy
Refining position using Least Squares Matching (LSM)
42 scenes of GF-1 MSS images, and 25 check points for each scene
Check the RMSEs of matched points before and after Least Squares Matching (LSM).
Slave tile
Master tile
Least Squares Matching

19. Online Matching Test data (reference: Bing aerial maps)
No.
Data source
Band
Image size
GSD
(m)
Elevation
Initial
Model 1
Landsat-5
Band 4
6850×5733 30
Rigorous 2
Cbers-4
Band 3
6000×6000 20 3
GF-1
4548×4595 8
41-825
RPC 4
ZY-3
Band 1
8819×9279
5.8 5
Theos
Pan
14083×14115
Affine 6
Band3
6000×88154
Test result (20 check points)
Test
No.
Required
GCPs
Correct
Matches
Run-time
(s)
Rectification
Model
RMSE
(pixel) 1 30 32
7.11
Rigorous
1.24 2
100 97
36.83
TD-RPC
1.63 3 36
9.67
RPC-Refine
1.38 4 35
23.48
1.82 5 77
67.41
1.46 6
36.93
2.01
Test 4: Before After
Test 5: Before After

20. Outline
Background
Technical frame
Online matching
Results
Conclusions

21. Conclusions Geometric rectification for RS images
Efficient, robust, accurate and well distributed
Automatically collect from online aerial maps
Free or cheap
Not suitable for dense matching ---- time consuming
Accuracy of online aerial maps is limited, but it is improving

22. Questions & Suggestions?