1. Multi-Information Based GCPs Selection Method
Yanfeng Gu, Zhimin Cao, Ye Zhang
Harbin Institute of Technology, China

2. Outline Background Bucket sampling The proposed methods
Experiments and results
5/7/2019
Harbin Institute of Technology

3. Background GCPs(Ground Control Points)
A location on the surface of the Earth (e.g., a road intersection, centre of special building, road corner, building corner) that can be identified on the imagery and located accurately on a map
road intersection centre of special shape road corner building corner
5/7/2019
Harbin Institute of Technology

4. Background The methods for extraction and matching of GCPs SIFT MSER
Scale Invariant Feature Transform, an algorithm in computer vision to detect and describe local features in images
MSER
Maximally Stable Extremal Region, to find correspondences between image elements from two images with different viewpoints
RANSAC
RANdom SAmple Consensus, an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers
5/7/2019
Harbin Institute of Technology

5. Background What problems of GCPs should be considered Number of GCPs
In terms of some specific applications, there are definite requirements for number of GCPs. (e.g. 39 for 3rd RFM)
Spatial Distribution of GCPs
The goal is to make the distribution of GCPs evenly for most of applications
5/7/2019
Harbin Institute of Technology

6. Bucket Method/Sampling
The ‘bucket’ is just a type of data structure which is open hashing with chaining
Step 1: the test region is divided into buckets. Each bucket may contain a number of GCPs.

7. Bucket Method
Step 2: the GCPs with minimum and maximum coordinates are chosen as the first two GCPs to be finally selected, the others will be assigned to corresponding buckets.
5/7/2019
Harbin Institute of Technology

8. Bucket Method
Step 3: the rest GCPs are selected from buckets which contain more than one GCP
5/7/2019
Harbin Institute of Technology

9. Bucket Method Advantage Disadvantage
Bucket method is able to get certain number of GCPs with evenly distribution to some extent
Disadvantage
Some useful information do not be considered
Height
Spatial region information
5/7/2019
Harbin Institute of Technology

10. The proposed methods 3D Bucket Multi-information based GCPs Selection
Bucket-based sampling
Use of height
Multi-information based GCPs Selection
Use of spatial correlation in local region
5/7/2019
Harbin Institute of Technology

11. Multi-information GCPs Selection
Input candidate GCPs
Calculate the corresponding gray correlation of each points in local
Classify the GCPs with the histogram of the correlation coefficients
Clustering subsets by k-means
Select GCPs from these clustered subsets
Output
5/7/2019
Harbin Institute of Technology

12. Multi-information GCPs Selection
Input candidate GCPs
Calculate the corresponding gray correlation of each points in local
Classify the GCPs with the histogram of the correlation coefficients
Clustering subsets by k-means
Select GCPs from these clustered subsets
Output
5/7/2019
Harbin Institute of Technology

13. Multi-information GCPs Selection
Input candidate GCPs
Calculate the corresponding gray correlation of each points in local
Classify the GCPs with the histogram of the correlation coefficients
Clustering subsets by k-means
Select GCPs from these clustered subsets
Output
5/7/2019
Harbin Institute of Technology

14. Experimental Results
Tested data : Avenches (downloaded from
Image Size: 1800*1800 pixels
Image resolution: 7.5cm/pixel
Note: the corresponding rigorous model is available for evaluation
450 pairs of GCPs extracted: SIFT+MSER+RANSAC
5/7/2019
Harbin Institute of Technology

15. Table 1. The Comparison of Relative Errors of horizontal coordinates
Experimental Results
Table 1. The Comparison of Relative Errors of horizontal coordinates 40 45 50 55 60 65 75
direct
0.0049
0.0022
5.55e-4
5.67e-4
3.62e-4
3.58e-4
4.70e-4
bucket
7.50e-4
6.07e-4
5.24e-4
4.75e-4
3.54e-4
2.90e-4
2.78e-4
3Dbucket
6.84e-4
4.05e-4
3.86e-4
2.97e-4
2.61e-4
2.40e-4
Multi-info
4.49e-4
3.44e-4
2.52e-4
2.14e-4
2.58e-4
2.22e-4
2.11e-4
5/7/2019
Harbin Institute of Technology

16. Table 2. The comparison of Relative Errors of vertical coordinates
Experimental Results
Table 2. The comparison of Relative Errors of vertical coordinates 40 45 50 55 60 65 75
direct
0.0168
0.0115
0.0076
0.0085
0.0047
0.0038
0.0070
bucket
0.0052
0.0042
0.0040
0.0037
0.0035
0.0034
0.0033
3Dbucket
0.0064
0.0053
0.0041
0.0036
Multi-info
0.0049
0.0044
0.0032
5/7/2019
Harbin Institute of Technology

17. Than you for your attention!
5/7/2019
Harbin Institute of Technology