Distribution optimization of ground control points in remote sensing image geometric rectification based on cluster analysis

Jingtao Zhang; Bo Cheng

doi:10.1117/12.833671

30 October 2009 Distribution optimization of ground control points in remote sensing image geometric rectification based on cluster analysis

Jingtao Zhang, Bo Cheng

Author Affiliations +

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 749707 (2009) https://doi.org/10.1117/12.833671
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

The Selection of ground control points (GCP) for remote sensing image geometric rectification is an important step. The number, distribution and accuracy of GCP have a direct impact on the effect of geometric rectification. With the development of remote sensing technology, GCP auto-matching algorithm automatically has access to many high precision GCP. However, very few studies of the distribution of GCP, which are also important to geometric rectification, are carried on. GCP should be evenly distributed in the whole image. However the understanding of evenly distribution has high subjectivity. In this paper, the method based on cluster analysis has been proposed to optimize the distribution of GCP. A subset of appropriate, even and high-precision GCP was filtered from a large number of GCP. Through the introduction of the concept of the monopolized circle, the uniform index was put forward to measure the uniformity of GCP pattern quantitatively. This paper also studied the relationship between number and precision of GCP. It is proved by experiment that the rest GCP after the algorithm of optimization were evenly distributed and achieved good results. At the same time, the efficiency and accuracy of image geometric rectification could be improved.

Citation Download Citation

Jingtao Zhang and Bo Cheng "Distribution optimization of ground control points in remote sensing image geometric rectification based on cluster analysis", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 749707 (30 October 2009); https://doi.org/10.1117/12.833671

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