摘要
在遥感影像几何校正方法中,通常认为精度最高的是共线方程模型。针对共线方程模型定向参数解算过程中误差方程的病态问题,提出了利用基于控制点的神经网络方法进行高分辨率遥感影像几何校正方法,并从理论上进行了可行性分析。实验证明,在具有一定数量控制点作为训练样本的条件下,应用BP和RBF神经网络进行遥感影像几何校正,可以达到比共线方程模型更高的精度;神经网络模型能够自动抑制含较大误差控制点对模型纠正精度的影响,在实际应用中可以提高几何校正效率。
Of all the methods for geometric rectification of remote sensing imagery, the Collinearity Equation Model is usually considered to have the best accuracy. Nevertheless, when the Collinearity Equation Model based on GCPs （Ground Control Points） is used to compute the elements of inner and exterior orientation, the coefficient matrix condition of the normal equation often becomes deteriorative, which greatly affects the accuracy of the orientation elements. In this paper, a new method for geometric rectification based on neural network is proposed , Experiments show that, under the precondition that a certain number of GCPs serve as the training data, the neural network ofBP and RBF can perform well in geometric rectification of remote sensing imagery and reach higher accuracy than the Collinearity Equation Model. Besides, the neural network can eliminate the influence of GCPs with gross error, and hence can better improve the efficiency.
出处
《国土资源遥感》
CSCD
2008年第1期19-22,63,共5页
Remote Sensing for Land & Resources
基金
国家自然科学基金(40671127)
“111”计划(B06004)
长江学者和创新团队发展计划共同资助
关键词
神经网络
几何校正
共线方程模型
Neural network
Geometric rectification
Collinearity equation model
