遥感图像几何校正的支持向量机算法研究

doi:10.3969/j.issn.1001-2400.2011.05.020

Abstract

Abstract:

Facing the disadvantages of geometric correction algorithm for the remote sensing image at present,a new algorithm is studied and we put forward the geometric correction algorithm and solving steps for the remote sensing image based on SVM, introduce the SVM theory and approach and adopt the essence theory of the remote sensing image approximate geometric correction. One testing region is selected, the ground control points coordinates and altitudes are surveyed by differential GPS, and the coordinates of the ground control points in the remote sensing image are measured with image processing software. We select a varying number of control points to correct the remote sensing image in the geometrical plain, and use other control points as testing points by the cluster algorithm in all the ground control points. We carry out remote sensing image geometric correction by the approximate geometric correction , the neural network model and the SVM algorithms, analyze and compare correction precision. Algorithm testing show that the algorithm for the SVM has good correction precision and generalizing ability. The algorithm for the SVM supports remote sensing application to develop the quantitative and accurate technique, and enlarges the geometric correction approach.

Key words: remote sensing image, image geometric correction, support vector machine(SVM), least squares method(LSM), correction precision

SHE Xiangyang;LI Chonggui;YAO Wanqiang. Research on the geometric correction algorithm for the remote sensing image by a support vector machine[J].J4, 2011, 38(5): 121-128+146.

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Research on the geometric correction algorithm for the remote sensing image by a support vector machine

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