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

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.