Adaptive speckle filters and scene heterogeneity

The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information     

Textural Analysis And Real-Time Classification of Sea-Ice Types Using Digital SAR Data

Digital measures of synthetic-aperture radar (SAR) image texture, as well as the local approximation to the mean value of individual ice types, were used to perform discrimination and mapping of ice types. The SAR data described in this paper were gathered in March, 1979, over the Beaufort Sea as part of the Canadian SURSAT project. Digital SAR data from a 3 × 3 km area were obtained using optical processing of the signal film and digital recording of the output image. Prior to performing the textural analysis, a digital filter algorithm was developed that minimizes the effect of radar-system-generated coherent speckle and produces an image approximating local tone while preserving edge definition. This image was used in the analysis to separate image tone from image texture. The textural analysis, which included calculating the entropy and inertia of the image, indicated that first- and multiyear, smooth- and rough-ice types could be distinguished based on the textural values obtained from the data with an overall accuracy of 65 percent. This study has also considered the use of cellular operations based upon neighborhood transformations to calculate the textural values. This computation method can potentially reduce the time to compute textural features on a general-purpose computer to near real-time rates.     

基于斑点方差估计的非下采样Contourlet域SAR图像去噪

 合成孔径雷达(SAR)图像固有的相干斑噪声严重影响图像质量,使得SAR图像的自动解译十分困难.本文联合SAR图像的统计特性和非下采样Contourlet对SAR图像细节信息的良好刻画能力,提出一种新的非下采样Contourlet域SAR图像去噪算法,通过估计到的各个高频方向子带的斑点噪声方差和变换系数模值的局部均值,对非下采样Contourlet变换系数进行判定,保留信号系数,抑制斑点噪声系数,实现SAR图像去噪.仿真实验结果表明,本文方法在斑点抑制的同时可以有效保持细节信息.     

基于相关视角多视校正的太赫兹阵列雷达散斑抑制方法

在针对人体安检的站开式太赫兹阵列雷达三维成像中,散斑效应严重影响了对体表隐藏违禁品的检测和识别性能,为此,对全息散斑进行有效抑制有着迫切的需要。利用目标旋转带来的角度自由度,结合目标散射对角度的敏感性,提出了一种基于相关视角多视校正的太赫兹全息雷达散斑抑制方法。在理论上推导了全息雷达散斑强度在相关角度照射下的归一化协方差表达式,给出了相关视角多视处理后的散斑对比度。在实验上利用340 GHz站开式MIMO阵列成像系统的快速成像的优势,对低速旋转目标在相关视角下的单视成像结果进行成像空间旋转校正、目标配准和多视处理。处理后散斑对比度降低至单视散斑的44％,散斑得到了有效抑制,目标轮廓明显,细节更加丰富。该方法能够在不增加系统复杂度的条件下有效抑制散斑噪声,实现图像增强。     

Resolution versus Speckle Relative to Geologic Interpretability of Spaceborne Radar Images: A Survey of User Preference

A survey was conducted to evaluate user preference for resolution versus speckle relative to the geologic interpretability of spaceborne radar images. Thirteen different resolution/looks combinations were simulated from Seasat synthetic-aperture radar (SAR) data of each of three test sites. The SAR images were distributed with questionnarires for analysis by eighty-five earth scientists. The relative discriminability of geologic targets at each test site for each simulation of resolution and speckle on the images is determined here from a survey of the evaluations. A large majority of the analysts indicate that for most targets a two-look image at the highest simulated resolution is best. For a constant data rate a higher resolution is more important for target discrimination than a greater number of looks. Sand dunes require more looks than other geologic targets. Multiple-look images are preferred over the corresponding single-look image at all resolutions. The number of multiple looks that is optimal for discriminating geologic logic targets is in general inversely related to the simulated resolution.     

一种基于图像结构保持降低SAR图像斑点噪声的非局部均值滤波

本文提出一种针对合成孔径雷达（SAR）图像保持结构的斑点去除（SPSR）非局部均值滤波算法,它基于图像的非局部自相似性。该SPSR算法的独特之处在于对相似结构中像素的辨识度强,因此可在散斑滤除的过程中避免图像模糊。为缓解散斑噪声对相似性测量的影响,两级过滤方案引入其中。滤波的第一阶段旨在得到一个结构相似性更精确的相似值,然后依据相似度大小对这区域实施强度不一的扩散滤波。与传统滤波器相比,该算法大大提高了散斑滤除的性能,同时,图像的结构保持更完好。     

SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed SAR images are accurately modeled by alpha-stable distributions, a family of heavy-tailed densities. Consequently, we exploit this a priori information by designing a maximum a posteriori (MAP) estimator. We use the alpha-stable model to develop a blind speckle-suppression processor that performs a nonlinear operation on the data and we relate this nonlinearity to the degree of non-Gaussianity of the data. Finally, we compare our proposed method to current state-of-the-art soft thresholding techniques applied on real SAR imagery and we quantify the achieved performance improvement.     

Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery

Radar images can show great variability from pixel to pixel, which is an obstacle to effective processing. This variability, due to speckle created by the radar wave coherence, necessitates the use of more adapted filters. Previous studies have shown that multiresolution wavelet analysis yields better results but produces artefacts due to multiscale decomposition. This paper proposes a method that reduces these effects by introducing the fractal dimension. The resultant filter combines wavelet decomposition and variance change model based on the level of variance estimated by studying the fractal dimension of the image.     

Analysis of speckle noise contribution on wavelet decomposition ofSAR images

This paper describes the use of the wavelet transform for multiscale texture analysis. One of the basic problems is that texture measures have to adapt to the peculiarity of radar images that contain multiplicative speckle noise. In this paper, the focus is on the effect of speckle on the wavelet transform. The effect is first assessed analytically. It is shown that the wavelet coefficients are modulated by the multiplicative character of the speckle in a manner that is proportional to the target mean backscattering coefficient. The effect of speckle correlation is also demonstrated. Wavelet decomposition is then applied to a simulated radar image generated by a Monte Carlo approach and based on a statistical model. Modeling shows that the correlation properties of speckle have an effect up to a scale that corresponds to its granular size. The results also show that the main contribution to the wavelet transform for an homogeneous area is the first-order statistical distribution of speckle, which remains important even at large scales. The results are then compared to a ERS-1 synthetic aperture radar (SAR) image of a primary tropical forest region     

基于复小波方向信息的SAR图像斑点噪声抑制

针对一般小波去噪方法在去除合成孔径雷达(Synthetic Aperture Radar-SAR)图像斑点噪声时不能有效保持图像边缘信息的问题,提出结合双密度双树复小波变换（Double-Density Dual Tree Complex Wavelet Transform –DD_DTCWT）方向信息进行边缘检测的SAR图像噪声抑制算法。本文对边缘检测指标进行改进,利用DD_DTCWT方向复小波系数的相对方差作为边缘检测指标,通过相对方差分布密度函数获取阈值处理的自适应门限,由此实现SAR图像的自适应滤波。实验结果表明,本文提出的边缘检测和主方向高频复系数提升方法可以有效保持并增强图像的边缘信息。与SRAD算法和基于DD_DTCWT的双变量收缩函数（Bivariate Shrinkage Function--BSF）算法相比较,本文算法具有更好的边缘保持能力。      

Evaluation of textural and multipolarization radar features forcrop classification

The aim of this research is to evaluate crop discrimination using airborne radar data based on multipolarization and textural information. Multipolarization data (C-HH, C-VV, and C-HV) were used for discriminating 5 crop types i.e., corn, wheat, soya, pasture, and alfalfa. For the multipolarization evaluation, an unsupervised classification algorithm and a supervised method based on maximum likelihood were used on the data. For the textural evaluation, textural measures of different degrees were calculated on three different order histograms and were evaluated from the crop discrimination point of view. Results show that multipolarization correct classification rates of 86.31% and 74.47% were obtained for supervised and unsupervised methods respectively. Hence, multipolarization radar data offer an adequate tool for crop identification especially with supervised classification. The evaluation of textural measures shows that for a first order histogram the mean measure gives the best rate of discrimination. In the case of second and third order histograms, the best measures are contrast and large number emphasis respectively. These textural measures were integrated with the three multipolarization channels in order to determine their specific contributions. Results show that crop class separability is thereby improved and that the rate of correct classification increased by 9.79% for the crops     

Speckle reducing anisotropic diffusion

This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.     

激光主动探测图像散斑自适应滤波抑制方法研究

激光主动照明成像具有作用距离远、系统分辨率高、可在低照度背景等复杂环境下获取目标图像等优点,但探测图像会受散斑噪声干扰.把高斯滤波、均值滤波和自适应滤波方法分别应用到仿真实验中进行散斑噪声抑制,实验表明:与高斯滤波和均值滤波相比,自适应滤波能有效抑制图像噪声,保留图像的边缘和细节信息.利用自适应滤波方法对获取的单帧和多帧累加平均的激光主动探测图像进行散斑抑制实验,使用散斑对比度进行定量分析,结果表明多帧短曝光图像累加平均可有效抑制图像的散斑噪声,自适应滤波可进一步降低图像的散斑噪声.     

Supervised classification of K-distributed SAR images of naturaltargets and probability of error estimation

A radiometric and textural classification method for the single-channel synthetic aperture radar (SAR) image is proposed, which explicitly takes into account the probability density function (pdf) of the underlying cross section for K-distributed images. This method makes extensive use of adaptive preprocessing methods (e.g. Gamma-Gamma MAP speckle filtering), in order to ensure good classification accuracy as well as fair preservation of the spatial resolution of the final result. Error rates can be estimated during the training step, allowing one to select only relevant reflectivity classes and to save computation time in trials. The classification method is based on a maximum likelihood (ML) segmentation of the filtered image. Finally, a texture criterion is introduced to improve the accuracy of the classification result     

Information-theoretic heterogeneity measurement for SAR imagery

A heterogeneity feature, calculable from synthetic aperture radar (SAR) images on a per-pixel basis, but relying on global image statistics, is defined and discussed. Starting from the multiplicative speckle and texture models relating the amount of texture and speckle to the local mean and variance at every pixel, such a feature is rigorously derived from Shannon's information theory as the conditional information of local standard deviation to local mean. Thanks to robust statistical estimation, it is very little sensitive to the noise affecting SAR data, and thus capable of capturing subtle variations of texture whenever they are embedded in a heavy speckle. Experimental results carried out on two SAR images with different degrees of noisiness demonstrate that the proposed feature is likely to be useful for a variety of automated segmentation and classification tasks.     

Synthetic aperture radar image compression using tree-structured edge-directed orthogonal wavelet packet transform

Currently, wavelet-based coding algorithms are popular for synthetic aperture radar (SAR) image compression, which is very important for reducing the cost of data storage and transmission in relatively slow channels. However, standard wavelet transform is limited by spatial isotropy of its basis functions that is not completely adapted to represent image entities like edges or textures, which means wavelet-based coding algorithms are suboptimal to image compression. In this paper, a novel tree-structured edge-directed orthogonal wavelet packet transform is proposed for SAR image compression. Inspired by the intrinsic geometric structure of images, the new transform improves the performance of standard wavelet by filtering along the regular direction first and then along the orthogonal direction with directional lifting structure. The cost function of best basis selection is designed by textural and directional information for tree-structured edge-directed orthogonal wavelet packet transform. The new transform including speckle reduction can be used to construct SAR image coder with the embedded block coding with optimal truncation for transform coefficients, and arithmetic coding for additional information. The experimental results show that the proposed approach outperforms JPEG2000 and Fast wavelet packet (FWP), both visually and item of PSNR values.     

Textural Features for Radar Image Analysis

Texture is an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in the analysis of a variety of photographic images, they have not been used for processing radar images. In this paper, we present a procedure for extracting a set of textural features for characterizing small areas in radar images and show that these features can be used for classifying segments of radar images corresponding to different geological formations.     

Two-dimensional adaptive block Kalman filtering of SAR imagery

A method for removing speckle from synthetic aperture radar (SAR) imagery by using 2-D adaptive block Kalman filtering is introduced. The image process is represented by an autoregressive model with a nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which taken into account not only the effect of speckles as multiplicative noise but also the effects of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as those of the additive noise     

Oriented speckle reducing anisotropic diffusion.

Ultrasound imaging systems provide the clinician with noninvasive, low-cost, and real-time images that can help them in diagnosis, planning, and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The speckle reducing anisotropic diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2-D synthetic image with two different levels of multiplicative noise and on a 3-D synthetic image of a Y-junction, and the new filter is applied on a 3-D real ultrasound image of the liver.     

Radar polarimetry: analysis tools and applications

The authors have developed several techniques to analyze polarimetric radar data from the NASA/JPL airborne SAR for Earth science applications. The techniques determine the heterogeneity of scatterers with subregions, optimize the return power from these areas, and identify probable scattering mechanisms for each pixel in a radar image. These techniques are applied to the discrimination and characterization of geologic surfaces and vegetation cover, and it is found that their utility varies depending on the terrain type. It is concluded that there are several classes of problems amenable to single-frequency polarimetric data analysis, including characterization of surface roughness and vegetation structure, and estimation of vegetation density. Polarimetric radar remote sensing can thus be a useful tool for monitoring a set of Earth science parameters