Coherence estimation in synthetic aperture radar data based on speckle noise modeling

In the past we proposed a multidimensional speckle noise model to which we now include systematic phase variation effects. This extension makes it possible to define what is believed to be a novel coherence model able to identify the different sources of bias when coherence is estimated on multidimensional synthetic radar aperture (SAR) data. On the one hand, low coherence biases are basically due to the complex additive speckle noise component of the Hermitian product of two SAR images. On the other hand, the availability of the coherence model permits us to quantify the bias due to topography when multilook filtering is considered, permitting us to establish the conditions upon which information may be estimated independently of topography. Based on the coherence model, two coherence estimation approaches, aiming to reduce the different biases, are proposed. Results with simulated and experimental polarimetric and interferometric SAR data illustrate and validate both, the coherence model and the coherence estimation algorithms.     

采用自适应邻域马尔可夫场的极化SAR图像分割

提出了一种新的马尔可夫场(MRF)极化SAR图像分割方法,对于图像中的每个像素,利用极化SAR数据总功率(span)图,得到该像素的马尔可夫场自适应邻域系统,且对区域标号过程的参数进行修正.使用该方法对机载极化SAR数据进行分割,实验结果表明,与传统的马尔可夫场方法、最大似然方法相比,该方法可以得到较好的分割结果.     

Remote sensing image segmentation using a Kalman filter-trained neural network

This article describes the application of a neural network to the segmentation of remote sensing images of multispectral SPOT and fully polarimetric SAR data. The structure of the network is a modified multilayer perceptron and is trained by the Kalman filter theory. The internal activity of the network is a nonlinear function, while the function at output layer is linearized through the use of a polynomial basis function, thus allowing us employ the theory of Kalman filtering as the learning rule. The network is therefore called the dynamic learning (DL) neural network. It is found that, when applied to SPOT and SAR data, the DL neural network gives a good segmentation results, while the learning rate is very promising compared to the standard backpropagation network and other fast-learning networks. In particular, for polarimetric SAR data, optimum polarizations for discriminating between different terrains are automatically built in through the use of the Kalman filter technique. The suitability and effectiveness of the proposed DL neural network to the segmentation of remote sensing images is demonstrated. © 1996 John Wiley & Sons, Inc.     

基于DInSAR方法监测南京地表沉降的结果与分析

针对InSAR数据预挑选过程中一直存在着人为的主观性问题,提出了基于相干系数的像对可用性判断因子的概念,利用DInSAR技术对可用性判断因子检测出的可用像对进行处理,获得了南京市区1996年8月到2000年12月间地表沉降结果。通过对沉降结果的时空分析,指出这期间南京地区没有发生持续性的沉陷漏斗,但该地区的地面沉降受地下水位的影响较大,呈现明显的季节性变化。     

基于自适应形态滤波的医学超声图像降噪

针对医学超声图像上的斑点噪声,本文提出一种基于自适应形态滤波的降噪方法.首先构造一组检测图像中不同像素值突变的结构因子;再对每个结构因子构造相应的形态滤波结构元;最后对每个像素点邻域进行结构检测,找到该点处最可能存在的突变结构,以相应的结构元完成该点的形态滤波.对不同信噪比的仿真图像和实际图像分别采用本文方法和各向异性扩散滤波,不同尺度传统形态滤波进行了:比较实验,结果表明:采用本方法可将超声图像的信噪比、对比度噪声比和图像优度分别平均提高15%、37%和69%,优于其它方法.     

极化合成孔径雷达图像船舶目标检测算法

结合区域划分和结构检测模板提出了改进极化白化滤波(IPWF)算法,利用IPWF算法融合极化合成孔径雷达(POL-SAR)中各极化通道图像,同时抑制相干斑,然后利用双参数恒虚警率(CFAR)检测方法对融合后的图像进行船舶目标检测.本文利用香港地区SIR-C全极化单视复数据进行了实验,结果表明IPWF算法更好地降低了相干斑因子,提高了船舶目标的检测率、控制了虚警率,同时可以更好地保持船舶目标的结构信息.     

SAR imaging using multidimensional continuous wavelet transform and applications to polarimetry and interferometry

Usual SAR imaging process makes the assumption that the reflectors are isotropic and white (i.e., they behave in the same way regardless the angle from which they are viewed and the emitted frequency within the bandwidth). The multidimensional continuous wavelet transform (CWT) in radar imaging was initially developed to highlight the image degradations due to these assumptions. In this article the wavelet transform method is widened to polarimetry and interferometry fields. The wavelet tool is first used for polarimetric image enhancement, then for coherence optimization in interferometry. This optimization by wavelets, compared with the polarimetric one, gives better results on the coherence level. Finally, a combination of both methods is proposed. © 2005 Wiley Periodicals Inc. Int J Imaging Syst Technol, 14, 206–212, 2004; Published online in Wiley Inter‐Science (www.interscience.wiley.com). DOI 10.1002/ima.20025     

利用InSAR相干系数探测地震引起的城市破坏程度

利用InSAR相干系数对SAR图像散射体特性的变化很敏感的特点,定义了一种相干系数变化指数,并证明了利用该相干性变化指数探测地震引起的城市破坏程度是可行的,最后以2003年伊朗Bam地区地震为例进行了试验验证研究。试验结果表明,建筑物的破坏等级与相干系数变化指数的大小高度相关,相干系数变化指数越大,建筑物破坏越大;相干系数变化指数相对较小,建筑物破坏较轻。根据定义的相干系数变化指数绘制出了Bam城地震破坏等级图,该等级图与国际地球灾害中心（ICG）提供的结果相一致。     

干涉雷达地表相干特性分析及土地类型分类

基于重轨干涉相干测量的基本原理，利用ERS－1／2重轨干涉雷达数据，对大部分地表有、无积雪覆盖时的地表的相干特性进行了分析，并在此基础上，利用地物的相干特性和振幅强度信息，对大部分地表夫积雪覆盖时的地表进行了土地类型的识别和分类。     

干涉SAR相干系数估计的快速算法

在SAR干涉测量中,相干系数是干涉图像对相位稳定性的衡量标准。干涉基线距和地表地形都影响相干系数的计算,在通常的相干系数的计算中,需要补偿由基线和地形引起的干涉相位,造成相干系数计算中计算量的大大增加。本文应用了一种不需要进行相位补偿的相干系数计算方法,并与通常的相干系数的计算方法相比较。实验结果证明,本算法在保证一定的相干系数计算精确性的基础上,大大提高了相干系数的计算效率,可以应用于快速浏览相干图像对,以评价其用于相干测量时的质量好坏。     

基于邻域重构模型的高分辨SAR图像精确配准

合成孔径雷达(SAR)的成像过程使其高分辨图像的几何形变呈现局部性.针对高分辨SAR图像精确配准问题,本文提出一种基于邻域重构模型的局部转换函数.邻域重构模型首先采用重构系数刻画参考图像中每个像素点的几何位置;接着给出了一种重构控制点的选择标准使每个像素的配准误差达到最小;最后根据重构系数及控制点坐标对输入图像进行再抽样以实现配准.与经典分片线性映射相比,该模型从理论上给出了一种区域剖分准则:对于每个像素选取能使配准误差能达到最小的控制点作为重构控制点.对模拟数据和真实SAR图像进行了试验,结果表明,该模型能有效地提高配准精度.     

干涉合成孔径雷达复图像配准精度分析和方法

基于干涉合成孔径雷达（Interfermetric Synthetic Aperture Radar-InSAR）的系统模型、干涉相位误差的统计特性，分析InSAR配准精度与干涉相位误差之间的确定关系，讨论复图像配准过程中存在的问题，给出实用的InSAR复图像配准方法。通过对SIR-C/X-SAR意大利Enta火山干涉数据的处理，得到了精确的配准结果。     

Adaptive-neighborhood speckle removal in multitemporal synthetic aperture radar images

We present a new method for multitemporal synthetic aperture radar image filtering using three-dimensional (3D) adaptive neighborhoods. The method takes both spatial and temporal information into account to derive the speckle-free value of a pixel. For each pixel individually, a 3D adaptive neighborhood is determined that contains only pixels belonging to the same distribution as the current pixel. Then statistics computed inside the established neighborhood are used to derive the filter output. It is shown that the method provides good results by drastically reducing speckle over homogeneous areas while retaining edges and thin structures. The performances of the proposed method are compared in terms of subjective and objective measures with those given by several classical speckle-filtering methods.     

Clustering of polarization-encoded images

Polarization-encoded imaging consists of the distributed measurements of polarization parameters for each pixel of an image. We address clustering of multidimensional polarization-encoded images. The spatial coherence of polarization information is considered. Two methods of analysis are proposed: polarization contrast enhancement and a more-sophisticated image-processing algorithm based on a Markovian model. The proposed algorithms are applied and validated with two different Mueller images acquired by a fully polarimetric imaging system.     

Phase statistics of interferograms with applications to synthetic aperture radar

Interferometric methods are well established in optics and radio astronomy. In recent years, interferometric concepts have been applied successfully to synthetic aperture radar (SAR) and have opened up new possibilities in the area of earth remote sensing. However interferometric SAR applications require thorough phase control through the imaging process. The phase accuracy of SAR images is affected by decorrelation effects between the individual surveys. We analyze quantitatively the influence of decorrelation on the phase statistics of SAR interferograms. In particular, phase aberrations as they occur in typical SAR processors are studied in detail. The dependence of the resulting phase bias and variance on processor parameters is presented in several diagrams.     

神经网络识别图像椒盐噪声的自适应滤波方法

为了使椒盐噪声不影响图像的后续处理,提出一种基于BP神经网络噪声检测的自适应开关滤波器来检测和滤除图像椒盐噪声.该方法利用像素值及其邻域特性作为像素点的描述即神经网络的输入,通过神经网络自动检测图像的噪声位置,据此保持非噪声点不变,对噪声点进行自适应窗口大小的均值滤波处理,且仅窗口内非噪声点参与均值运算.实验结果表明,...     

Noise-residue filtering of interferometric phase images

The effect of noise-induced phase inconsistency on interferometric phase information content is studied. Phase inconsistencies, or residues, hinder a correct unwrapping of the phase signal. The probability of noise-induced phase inconsistencies is obtained as a function of the signal-to-noise ratio. Moreover, a two-dimensional noise-residue filter, intended to be applied as a preprocessing step before phase unwrapping, is proposed. The method is based on the observation that the noise creates adjacent phase inconsistencies mainly in interferometric phase images. A local analysis leads to a set of rules to be applied to reduce noise-induced phase inconsistencies. The filter performances are tested on noisy synthetic and real phase data.     

Speckle reduction in optical coherence tomography by adaptive total variation method

An adaptive total variation method based on the combination of speckle statistics and total variation restoration is proposed and developed for reducing speckle noise in optical coherence tomography (OCT) images. The statistical distribution of the speckle noise in OCT image is investigated and measured. With the measured parameters such as the mean value and variance of the speckle noise, the OCT image is restored by the adaptive total variation restoration method. The adaptive total variation restoration algorithm was applied to the OCT images of a volunteer’s hand skin, which showed effective speckle noise reduction and image quality improvement. For image quality comparison, the commonly used median filtering method was also applied to the same images to reduce the speckle noise. The measured results demonstrate the superior performance of the adaptive total variation restoration method in terms of image signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, and mean square error.     

An Adaptive Image Calibration Algorithm for Steganalysis

In this paper, a new adaptive calibration algorithm for image steganalysis is proposed. Steganography disturbs the dependence between neighboring pixels and decreases the neighborhood node degree. Firstly, we analyzed the effect of steganography on the neighborhood node degree of cover images. Then, the calibratable pixels are marked by the analysis of neighborhood node degree. Finally, the strong correlation calibration image is constructed by revising the calibratable pixels. Experimental results reveal that compared with secondary steganography the image calibration method significantly increased the detection accuracy for LSB matching steganography on low embedding ratio. The proposed method also has a better performance against spatial steganography.     

Real-time adaptive image impulse noise suppression

In this paper, a new intelligent hardware module suitable for the computation of an adaptive median filter is presented for the first time. The function of the proposed circuit is to detect the existence of impulse noise in an image neighborhood and apply the operator of the median filter only when it is necessary. The noise detection procedure can be controlled so that a range of pixel values is considered as impulse noise. In this way, the blurring of the image in process is avoided, and the integrity of edge and detail information is preserved. Experimental results with real images demonstrate the improved performance. The proposed digital hardware structure is capable to process gray-scale images of 8-bit resolution and is fully pipelined, whereas parallel processing is used in order to minimize computational time. In the presented design, a 3/spl times/3 or 5/spl times/5 pixel image neighborhood can be selected for the computation of the filter output. However, the system can be easily expanded to accommodate windows of larger sizes. The proposed digital structure was designed, compiled and simulated using the MAX+PLUS II Programmable Logic Development System by Altera Corporation. For the implementation of the system the EPF10K200SRC240-1 field-programmable gate array device of the FLEX10KE device family is utilized, and it can be used in industrial imaging applications where fast processing is required. The typical clock frequency is 65 MHz.