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

Outline Background Bucket sampling The proposed methods Experiments and results 5/7/2019 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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.

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 guyf77@gmail.com Harbin Institute of Technology

Bucket Method Step 3: the rest GCPs are selected from buckets which contain more than one GCP 5/7/2019 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

Experimental Results Tested data : Avenches (downloaded from http://www.isprs.org/data/avenches/default.aspx) 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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

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 guyf77@gmail.com Harbin Institute of Technology

Than you for your attention! 5/7/2019 guyf77@gmail.com Harbin Institute of Technology