地基SAR干涉相位滤波优化方法

地基SAR对边坡进行形变监测过程中,为了获得较高的图像质量和进行相位解缠的需要,往往要对干涉相位进行滤波优化,以提高干涉相位图的信噪比,减少干涉相位图中的残差点,从而有助于更好地进行相位解缠,提高形变监测的精度与可行性。本文针对地基SAR干涉相位图的空变等特性,对地基SAR干涉相位滤波算法进行了研究,着重介绍了四种经典、高效的干涉相位滤波算法,并进行了地基SAR边坡形变监测试验,使用实测干涉数据进行了四种滤波处理,从定量及定性的角度对每种滤波算法进行了比较与评估。最终给出了最优滤波算法,为地基SAR进行干涉相位实时滤波优化及后续数据处理提供了基础。      

EQUIVALENT BASELINE AND INTERFEROMETRIC PHASE OF CLUSTER SATELLITE SAR

The change of the equivalent baseline and interferometric phase of cluster SAR satellites is analyzed when the constellation circles around the Earth and the satellites rotate around the center at the same time. The letter provides assessment of baseline error and phase error which influence the precision of height measurement in the across-track interferometric mode. The mathematical model of cluster satellite movement is built, simulation analyses and the curve of height error are presented. The simulation results show that height measurement error can be compensated by the formulae derived in this letter, therefore, the Digital Elevation Models (DEM‘s) are recovered accurately.     

Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors

Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free component has some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum ${a quad posteriori}$ (AMAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intrascale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the state-of-the-art techniques qualitatively and quantitatively.      

应用小波分析对图像进行降噪处理

对传统傅里叶变换与小波变换进行理论分析比较,运用小波分析中小波变换具有对信号的自适应特性可以消除实际信号噪声中的高频成分,通过使用Matlab-6.51中的Wavelet工具箱可实现对图片的降噪操作,最终达到降噪效果。     

Speckle removal from SAR images in the undecimated wavelet domain

Speckle reduction is approached as a minimum mean-square error (MMSE) filtering performed in the undecimated wavelet domain by means of an adaptive rescaling of the detail coefficients, whose amplitude is divided by the variance ratio of the noisy coefficient to the noise-free one. All the above quantities are analytically calculated from the speckled image, the variance and autocorrelation of the fading variable, and the wavelet filters only, without resorting to any model to describe the underlying backscatter. On the test image Lena corrupted by synthetic speckle, the proposed method outperforms Kuan's local linear MMSE filtering by almost 3-dB signal-to-noise ratio. When true synthetic aperture radar (SAR) images are concerned, empirical criteria based on distributions of multiscale local coefficient of variation, calculated in the undecimated wavelet domain, are introduced to mitigate the rescaling of coefficients in highly heterogeneous areas where the speckle does not obey a fully developed model, to avoid blurring strong textures and point targets. Experiments carried out on widespread test SAR images and on a speckled mosaic image, comprising synthetic shapes, textures, and details from optical images, demonstrate that the visual quality of the results is excellent in terms of both background smoothing and preservation of edge sharpness, textures, and point targets. The absence of decimation in the wavelet decomposition avoids typical impairments often introduced by critically subsampled wavelet-based denoising.     

A Bayesian filtering technique for SAR interferometric phase fields

SAR interferograms are affected by a strong noise component which often prevents correct phase unwrapping and always impairs the phase reconstruction accuracy. To obtain satisfactory performance, most filtering techniques exploit prior information by means of ad hoc, empirical strategies. In this paper, we recast phase filtering as a Bayesian estimation problem in which the image prior is modeled as a suitable Markov random field, and the filtered phase field is the configuration with maximum a posteriori probability. Assuming the image to be residue free and generally smooth, a two-component MRF model is adopted, where the first component penalizes residues, while the second one penalizes discontinuities. Constrained aimulated annealing is then used to find the optimal solution. The experimental analysis shows that, by gradually adjusting the MRF parameters, the algorithm filters out most of the high-frequency noise and, in the limit, eliminates all residues, allowing for a trivial phase unwrapping. Given a limited processing time, the algorithm is still able to eliminate most residues, paving the way for the successful use of any subsequent phase unwrapping technique.     

Sideband noise of a large phase carrier in interferometric sensors

Interferometric sensors using balanced detectors and phase stabilisation schemes remain susceptible to laser intensity noise if phase carrier techniques are employed. An intensity stability of 4 × 10?6 is required for the low-frequency side-band noise to be equivalent to microradian phase shift sensitivity if the phase carrier has a peak phase shift of ?/2 rad.     

Modeling heterogeneous network traffic in wavelet domain

Heterogeneous network traffic possesses diverse statistical properties which include complex temporal correlation and non-Gaussian distributions. A challenge to modeling heterogeneous traffic is to develop a traffic model which can accurately characterize these statistical properties, which is computationally efficient, and which is feasible for analysis. This work develops wavelet traffic models for tackling these issues. We model the wavelet coefficients rather than the original traffic. Our approach is motivated by a discovery that although heterogeneous network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are all “short-range” dependent. Therefore, a simple wavelet model may be able to accurately characterize complex network traffic. We first investigate what short-range dependence is important among the wavelet coefficients. We then develop the simplest wavelet model, i.e., the independent wavelet model for Gaussian traffic. We define and evaluate the (average) autocorrelation function and the buffer loss probability of the independent wavelet model for fractional Gaussian noise (FGN) traffic. This assesses the performance of the independent wavelet model, and the use of which for analysis. We also develop (low-order) Markov wavelet models to capture additional dependence among the wavelet coefficients. We show that an independent wavelet model is sufficiently accurate, and a Markov wavelet model only improves the performance marginally. We further extend the wavelet models to non-Gaussian traffic through developing a novel time-scale shaping algorithm. The algorithm is tested using real network traffic and shown to outperform FARIMA in both efficiency and accuracy. Specifically, the wavelet models are parsimonious, and have a computational complexity O(N) in developing a model from a training sequence of length N, and O(M) in generating a synthetic traffic trace of length M     

利用残余点优选InSAR干涉相位条纹图的研究

该文基于相位展开中残余点的概念,深入研究了残余点发生概率与配准精度间的内在联系,根据所得出的模型,提出以残余点的数目作为衡量不同配准结果的准则。挑选出最佳的条纹图,从而最大程度地减小数字高程图中因配准所引起的误差．实际数据的研究表明,该方法简便精确、行之有效．     

Least squares phase unwrapping in wavelet domain

Two-dimensional phase unwrapping is an important processing step in some coherent imaging applications. Least squares phase unwrapping is one of the robust techniques used to solve two-dimensional phase unwrapping problems. However, owing to its sparse structure, the convergence rate is very slow, and some practical methods have been applied to improve this condition. A new method for solving the least squares two-dimensional phase unwrapping problem is presented. This technique is based on the multiresolution representation of a linear system using the discrete wavelet transform. By applying the wavelet transform, the original system is decomposed into its coarse and fine resolution levels. Fast convergence in separate coarse resolution levels makes the overall system convergence very fast.     

小波分析在图像降噪中的应用

本文在对图像降噪进行总体概述的基础上,介绍了传统降噪和小波降噪的原理,提出一种以阈值降噪法为基础的混合算法。然后用MATLAB中的小波工具箱对一个含有噪声图像进行降噪。通过实验结果的对比,可以看出新算法可以更为有效地降低噪声,并较好地保留图像的细节。     

交轨干涉SAR涌浪干涉相位模型及数值模拟

建立了包含海表面高度和速度聚束的交叉轨迹干涉合成孔径雷达(AcrossTrack Interferometric Synthetic Aperture Radar,XTI-SAR)涌浪干涉相位模型,得到了涌浪成像的解析表达式.进一步研究了XTI-SAR沿方位向传播的涌浪成像机制.定义二次谐波振幅与基波振幅比率来表征成像非线性,通过比较XTI-SAR和沿轨迹干涉合成孔径雷达(Along-Track Interferometric Synthetic Aperture Radar,ATI-SAR)相位的二阶调和分量,分析不同海况和干涉SAR参数情况下的数值模拟,结果表明:当速度聚束弱时,XTI-SAR相位比ATI-SAR相位具有较强的非线性,ATI-SAR比XTI-SAR更适合测量海浪.当速度聚束强时,XTI-SAR相位比ATI-SAR相位具有较弱的非线性,XTI-SAR比ATI-SAR更适合测量海浪.     

干涉合成孔径雷达相位滤波的一种新算法

该文着重就目前国际上研究的一个热点干涉合成孔径雷达成像中的相位滤波问题进行了讨论。提出了一种新的相应滤波方法,可适应干涉条纹方向的变化,十分适用于对干涉条纹密集的区域进行滤波。通过对德国X-SAR数据的处理检验该方法的效果,结果相当理想。此外,这种方法实现起来相当简单。     

Dual learning based compression noise reduction in the texture domain

Compression noise reduction is similar to the super-resolution problem in terms of the restoration of lost high-frequency information. Because learning-based approaches have proven successful in the past in terms of addressing the super-resolution problem, we focus on a learning-based technique for compressed image denoising. In this process, it is important to search for the exact prior in a training set. The proposed method utilizes two different databases (i.e., a noisy and a denoised database), which work together in a complementary way. The denoised images from the dual databases are combined into a final denoised one. Additionally, the input noisy image is decomposed into structure and texture components, and only the latter is denoised because most noise tends to exist within the texture component. Experimental results show that the proposed method can reduce compression noise while reconstructing the original information that was lost in the compression process, especially for texture regions.     

SAR interferometric signatures of forest

The potential of SAR interferometry for forest mapping and monitoring is discussed. It is shown that forest can clearly be discriminated from other land categories. Furthermore it is possible to distinguish a number of forest types. The presented approach is based on the SAR interferometric correlation and the backscatter intensities using ERS-1 SAR repeat-pass data. Baseline, time interval, and seasonal dependences were analyzed, substantiating a wide applicability of the approach. Data over an Alaskan test site were used to extend the results found over temperate forest to boreal forest and to demonstrate the potential of the described technique over remote areas. In addition, repeat-pass SAR interferometry was found to be particularly sensitive to change. Examples for the recognition of freezing, mechanical cultivation of agricultural fields, and canopy growth are shown     

SAR speckle reduction using wavelet denoising and Markov random field modeling

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-field-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model, and the iterated-conditional-modes method is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.     

Novel passive phase noise cancelling technique for interferometric fibre optic sensors

A novel technique for the cancellation of phase noise in a fibre interferometer caused by laser frequency emission jitter is described. The scheme is based on the use of a reference interferometer and phase generated carrier passive homodyne phase detection. Signal processing involving cross-multiplication of the sine and cosine components of the sensor and reference interferometer outputs is shown to provide cancellation of phase noise.<>     

小波分析在航天系统降噪处理中的应用

航天系统噪声是航天系统设备的顽疾,它干扰有用信号的调制、发射、传输、接收和解调,严重影响数据的精度。为了解决航天系统设备存在的噪声问题,结合小波降噪原理,提出了基于小波分析的降噪处理方法。利用Matlab软件设计了小波降噪程序,并对降噪过程进行了仿真。结果表明,基于小波分析的降噪处理方法可有效降低航天系统设备噪声。     

基于小波包分析的ECG信号降噪

在临床医学检测上,心电信号(ECG)是一种典型的人体生理信号,在采集时常伴有工频干扰、基线漂移和肌电干扰等影响。为后续能进行有效诊断,必须对采集的ECG信号进行预处理。文中介绍了一种基于小波包分析的ECG信号降噪方法,首先根据熵标准选择最佳小波包基,然后对ECG信号进行多层小波包分解,最后对分解系数进行阈值量化后得到重构信号。通过对胎儿ECG信号的仿真实验,结果表明,小波包分析方法比傅里叶变换和小波变换等具有更好的降噪效果。     

基于小波域Fisher分类器的SAR图像变化检测

给出了一种无监督SAR图像变化检测算法,它不需要分布假设,而是通过联合灰度直方图的分布特性进行判别.算法利用自适应边缘检测提取训练数据,通过Fisher分类器对联合直方图进行判别分析,得到不同小波层待检测点隶属度,并根据邻域关系以及上下文进行融合,得到最终检测结果.对真实SAR图像进行检测,得到了较好的检测结果.