SAR image despeckling with a multilayer perceptron neural network

Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.     

自交叉双边滤波的极化SAR数据相干斑抑制

相干斑抑制是极化SAR数据预处理的关键步骤。双边滤波是一种空域和值域滤波相结合的优秀边缘保持滤波算法。针对双边滤波在抑制极化SAR数据相干斑的不足,该文将改进的交叉双边滤波引入到极化SAR数据降噪领域,加入散射机制测度来扩展原权重核,根据SPAN图像的局域变差系数自动调整空间方差系数,利用参考图像来度量灰度值和散射机制相似性。实验结果表明:本文方法较经典滤波算法有更强的噪声平滑能力和更好的细节信息保持能力,在保持原数据极化散射信息方面也表现出良好的性能,这为基于极化SAR数据的后续应用提供了支持。     

基于复contourlet域隐马尔科夫树模型的海面溢油合成孔径雷达图像相干斑抑制

海面溢油SAR图像中的相干斑噪声严重影响了后续的图像分割、特征提取和分类.为了更有效地抑制海面溢油SAR图像相干斑,文中提出了一种基于复contourlet域隐马尔科夫树模型的海面溢油SAR图像相干斑抑制方法.首先对观测图像取对数并进行复contourlet变换;然后在复contourlet域中用隐马尔科夫树模型对相邻尺度间的带通方向子带系数进行建模,并依据贝叶斯最小均方误差准则估计无噪系数;最后进行逆复contourlet变换和指数变换,得到相干斑抑制后的图像.大量实验结果表明,与Lee、Kuan、Frost及Gamma Map等4种经典滤波方法以及小波域和contourlet域隐马尔科夫树模型方法相比,文中方法从主观视觉和客观定量评价两方面来看综合性能更为优越,是一种行之有效的SAR遥感图像海面溢油检测的预处理方法.     

PolSAR相干斑抑制中的非局部均值滤波研究进展

为促进非局部均值滤波在PolSAR相干斑抑制技术领域的应用与发展,首先简要阐述了非局部均值滤波的基本理论;然后系统梳理了非局部均值在PolSAR相干斑抑制处理中应用的历史沿革,最后深入探讨了现有研究工作的不足之处及其解决之道,为该方向进一步开展研究工作提供有益参考。     

NSCT域统计模型的SAR图像相干斑抑制

本文对SAR图像在引进加性模型的基础上,采用非下采样Contourlet变换,根据其系数、邻域系数及父系数3者之间的相关性,给出一个分类准则,把系数分为2类:重要系数和非重要系数,然后采用改进的Donoho阈值处理重要系数,估计出不含噪声的非下采样Contourlet变换系数,从而得到抑制了相干斑的SAR图像。对真实SAR图像进行相干斑噪声抑制实验,结果显示本文方法在抑斑效果和图像的细节保留上均优于目前的许多方法。