航天光学遥感器在轨调制传递函数神经网络评价方法

通过对航天光学遥感器在轨调制传递函数模型和遥感图像的分析,找出遥感图像中与调制传递函数有关的特征信息,采用神经网络为工具,完成利用遥感器传输下来的任意一幅地面景物图像进行调制传递函数的评价。首先模拟出包含不同调制传递函数等级的遥感图像,组成训练样本集,再从图像中分别提取出直接与调制传递函数有关的特征参量和与景物结构有关的特征参量,作为神经网络的输入,网络通过对训练样本集中模拟出的大量调制传递函数已知的遥感图像训练后,当再次输入一幅调制传递函数未知的遥感图像时,便能够正确估计出其调制传递函数值。这种方法不需要在地面铺设靶标或预先获得调制传递函数已知的同一地面景物的航空图像作为参考,只需获得任意一幅地面景物图像即可完成对遥感器调制传递函数的评价。实验结果表明,当不考虑噪声对调制传递函数的影响时,对调制传递函数的评价误差约为6%,而在考虑噪声时,评价误差约为9%。

Assessment Method of Modulation Transfer Function of On-Orbit Space Optical Remote Sensor Using Neural Network

Through analyzing the model of modulation transfer function(MTF of on-orbit space optical remote sensor(SORS and remote images,the eigenvectors related to MTF in the images are found out,using neural network(NN,complete assessing the MTF of space optical remote sensor through images of any landscape.First,remote images with different MTF levels were simulated,and they were used as the training base of NN,then the eigenvectors related to MTF directly and that related to landscape structure were abstracted respectively,and they were used as the inputs of NN,after being trained by a great lot of simulated images in the training base that MTF are known,the ANN could assess the MTF of totally unknown images.This method could assess the MTF of SORS through images of any landscape,and needn't the special views on the ground or aerial images as reference.The experiment results show that when noise is not considered,the mean assessment error is approximately 6%,or else,the mean error is approximately 9%.