基于Kohonen网络的小波阈值在遥感图像去噪中的应用研究  

Study and Application of Wavelet Threshold Based on Kohonen Neural Network in Remote Sensing Image De-noising

作　　者：张迅[1] 武文波[1] 

机构地区：[1]辽宁工程技术大学测绘与地理科学学院,辽宁阜新123000

出　　处：《计算机测量与控制》2008年第9期1312-1315,共4页Computer Measurement &Control

基　　金：教育部博士点基金资助项目(20050147002);辽宁省自然科学基金资助项目(20042175)

摘　　要：传统Donoho非线性小波阈值去噪方法中,阈值的选取一般是根据噪声方差设置的,但在实际的操作中很难对噪声方差的精确值,这种理论上的局限性使得各种基于方差估计的去噪方法并不能获得实际需要的去噪结果;针对阈值选取的问题,提出一种非线性小波变换阈值的kohonen神经网络的自调整学习训练方法对遥感图像进行去噪,该方法在小波变换的基础上结合了神经网络的非线性阈值自组织特征映射算法,阈值的选择根据训练图像进行学习;可以实现自调整寻找最优值,以满足实际,达到最优的去噪效果。In the de-noising of traditional non-linear wavelet threshold, we often select threshold by variance of noise, but that is difficultly obtained in practice, the disadvantage of theory cause that all kinds of the method based-on estimate of variance can＇t satisfy practical demand. Aim at thresholds choice, the paper propose new de-noising method of non-linear wavelet threshold based on Kohonen neural network in remote sensing image, which combines character mapping of threshold self- organize and neural network, chooses the best threshold according to the images, and reaches the best effect of de-noising.

关 键 词：遥感图像 去噪 小波变换 阈值 KOHONEN神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]