The Preprocessing Method of Control Points in Geometric Correction for UAV Remote Sensing Image

DOI

10.2991/icamcs-16.2016.146How to use a DOI?

Keywords

Control points, preprocessing method, K-means algorithm, Huffman tree.

Abstract

This paper focuses on the preprocessing method of control points in geometric correction for UAV (Unmanned Aerial Vehicle) remote sensing image. Control points preprocessing refers to find out the calibration points from the selected control points, so as to use the calibration points to fit the geometric correction function. Because in traditional K-means algorithm, clustering results have a strong dependence on initial clustering centers, so selecting initial clustering centers randomly will lead to the clustering results instability when preprocessing control points with traditional K-means algorithm, and it will influence the effect of the UAV remote sensing image geometric correction. Therefore, the paper imports the thought of Huffman tree to traditional K-means algorithm, aiming at optimizing the selection of initial clustering centers and improving the effect of UAV remote sensing image geometric correction ultimately.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lirong Diao
AU  - Riuan Liu
AU  - Tingting Chen
PY  - 2016/06
DA  - 2016/06
TI  - The Preprocessing Method of Control Points in Geometric Correction for UAV Remote Sensing Image
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 721
EP  - 725
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.146
DO  - 10.2991/icamcs-16.2016.146
ID  - Diao2016/06
ER  -