Study on segmentation and interpretation of multi-look polarimetric SAR images

The segmentation and interpretation of multi-look polarimetric synthetic aperture radar (SAR) images is studied. We first introduce a multi-look polarimetric whitening filter (MPWF) to reduce the speckle in multi-look polarimetric SAR images. Then, by utilizing the wavelet multiresolution approach to extract the texture information in different scales and the Markov random field (MRF) model to characterize the spatial constraints between pixels in each scale level, a multiresolution segmentation algorithm (MSA) to segment the speckle-reduced SAR images is presented. The MSA first segments the image at the lowest resolution level and then proceeds to progressively higher resolutions until individual pixels are well classified. An unsupervised step to estimate both the optimal number of texture classes and their model parameters is also included in the MSA so that the segmentation can be implemented without supervision. Finally, in order to interpret the results of the unsupervised segmentation and to understand the whole polarimetric SAR image, we develop an image interpretation approach which jointly utilizes the scattering mechanism identification and target decomposition approaches. Experimental results with the real-world multi-look polarimetric SAR image demonstrate the effectiveness of the segmentation and interpretation approaches.     

Analysis of JERS-1 (Fuyo-1) SAR data for vegetation discrimination in northwestern Brazil using the semivariogram textural classifier (STC)

Classification of JERS-1 (Fuyo-1) SAR data from the northwestern portion of Brazil was performed using the semivariogram textural classifier (STC). This is a deterministic, supervised classifier which provides the option of combining textural and radiometric information. Textural information is expressed by the semivariogram function; radiometric information is conveyed by the mean digital number (DN) value. Results have shown that STC allows vegetation units and water bodies to be discriminated and tentatively mapped, suggesting that this is a promising approach for environmental monitoring of rainforest regions using SAR data.     

Segmentation of SAR images

This paper presents a new algorithm for segmentation of SAR images based on threshold estimation using the histogram. The speckle distribution in the SAR image is modeled by a Gamma function. Thus, the SAR image histogram exhibits a combination of Gamma distributions. The maximum likelihood technique is therefore used to estimate the histogram parameters. This technique requires knowledge of the number of modes of the histogram, the number of looks of the SAR image, and the initial parameters of the histogram. The second derivative of the histogram is used to estimate the number of modes. We use two methods to estimate the number of looks. Initial parameters are estimated at the maximum of the Gamma function. Thresholds are selected at the valleys of a multi-modal histogram by minimizing the discrimination error between the classes of pixels in the image. The algorithm is applied to several RADARSAT SAR images with different number of looks. The results obtained are promising.     

基于遗传算法的SAR图像道路网检测方法

提出了一种基于遗传算法的SAR图像道路网检测算法.该算法以道路在SAR图像中呈黑色直线状结构为基本出发点,首先检测线特征点以获取潜在道路点;接着利用基于每个连通区域上的Radon变换提取线基元;然后从图像上最长的线基元出发,以其为种子基元,在其周围确定一个搜索区域,用遗传算法选择与种子基元共线的线基元进行连接,并更新种子基元,直到完成所有的连接,得到候选道路段.为了使检测道路更准确,利用蛇模型调整道路段的位置,然后用道路的特征进行鉴别.最后检测道路的交叉点,完成整个道路网的检测.机载SAR图像的实验结果及定量分析均证明了该算法的有效性.     

改进的基于Parzen窗算法的SAR图像目标检测

传统的Parzen窗检测算法假设目标占整个背景中较小的一部分,将SAR图像中的所有像素用于估计杂波概率密度函数,容易造成检测阈值的增大从而对不太明显的SAR图像舰船目标产生漏检。对此,提出了一种改进的Parzen窗检测算法,该算法通过自适应地设置目标窗口,将潜在的目标从检测图像中剔除,对剔除后的杂波背景采用Parzen窗进行非参数化的杂波模型估计,进而确定检测阈值,完成目标的检测。相比传统的Parzen窗检测算法,提出的SAR图像舰船目标检测算法减少了漏检数量,改善了检测性能。实测SAR图像的检测结果表明了该方法的有效性。     

Different processing levels of SIR-C/X-SAR radar data for the correction of relief induced distortions in mountainous areas

The third Shuttle Imaging Radar (SIR-C) was the first multifrequency and multipolarization SAR system monitoring the Earth from space. The availability of several frequencies and polarizations offers the opportunity of low level radiometric enhancement of SAR data via principal component or ratio analysis to correct the relief induced distortions of SAR images in mountainous areas. The potential of this method is presented based on the example of the Timna Valley in the south of Israel. Especially in areas where no DEM for the correction is available the method to be presented has a high application potential. For comparison the SIR-C/X-SAR data were also corrected conventionally on the basis of the local incidence angle derived via a DEM. This correction technique has the great advantage of getting real backscatter values for each frequency and polarization.     

基于模糊方向滤波的SAR 图像增强与去噪

针对SAR 固有的乘性噪声使其图像可解释性变差的问题, 基于方向滤波器和模糊逻辑探讨模糊方向滤波法。首先根据对比度来选取特征平面, 将图像进行特征平面增强并模糊化, 并获得模糊参数, 然后根据模糊规则计算隶属度, 以具有相应最大隶属度的像素构成最后滤波结果。SAR 实验由半点指数和正则均方差来检验不同方法的性能, 数据和图像均表明此方法优于Lee 滤波法、Esafa 法和加权形态滤波法, 既滤除噪声又保留图像细节; 为SAR 图像处理提供了有效的思路和工具。     

基于工作流的星载SAR辐射定标系统研究与设计

在对原有星载SAR辐射定标处理系统的开发和认识的基础上,分析星载SAR辐射定标处理系统的领域需求及辐射定标数据的处理流程,并对工作流技术及工作流管理系统机制进行研究.在现有的SAR辐射定标处理系统中引入工作流技术及设计思想,实现系统处理的控制逻辑与具体的实现逻辑分离,及辐射定标数据处理的自动化或半自动化,提高数据处理的效率以及系统的灵活性和适应性.     

Incorporating global-local a Priori knowledge into expectation-maximization for SAR image change detection

Speckle is one of the inevitable obstacles related to synthetic aperture radar (SAR) image change detection; it increases the overlap between changed and unchanged pixels in the histogram of a difference image. This makes the selection of a statistic model more difficult for describing opposite classes. To address this issue, this article developed an unsupervised change-detection approach for multitemporal SAR images that specifies a priori knowledge about the spatial characteristics of the classes through Dempster-Shafer evidence theory and embeds it into the Expectation-Maximization (EM) iteration process. It is based on the consideration that each pixel in the difference image is unique due to its neighbourhood, although some of them may have the same pixel value. Thus, under the hypothesis that local and global a priori knowledge are independent sources, a global-local a priori model is developed through Dempster-Shafer evidence theory. The EM algorithm allows one to estimate the statistical parameters of the opposite classes associated with this a priori model. As a consequence, the change-detection result can be obtained within the framework of Bayes. Visual and quantitative results obtained on real multitemporal SAR image data sets confirm the effectiveness of the proposed method compared with state-of-the-art ones for SAR image change detection.     

SAR image despeckling by combining saliency map and threshold selection

Speckle suppression and detail preservation are a unity of contradiction during synthetic aperture radar (SAR) despeckling. In this article, an effective despeckling method is proposed based on a self-adaptive neighbourhood, which takes into account the trade-off between homogeneous region suppression and point target preservation. The size and shape of the neighbourhood depend on an adaptive threshold that is treated as a linear function of a saliency map and a threshold range estimated by the Monte Carlo algorithm based on a given significance level and the number of looks in SAR. If the saliency of the current pixel is large, which always means that the current pixels should belong to a point target, a linear object, or an edge, a relatively large threshold will be assigned to search a neighbourhood with a small size. Conversely, pixels with a small saliency value will obtain a non-local neighbourhood with a large size. A maximum-likelihood estimation is employed to estimate the real radar reflectivity based on the pixels in the adaptive neighbourhood. The comparison experiments on simulated and actual SAR data validate the effectiveness of the proposed algorithm and demonstrate its overall speckle filtering characteristics compared with other algorithms. The visual and numerical experimental results show that the proposed despeckling method provides superior performance compared with several state-of-the-art despeckling methods.     

基于遗传算法的SAR图像自动道路提取

为了有效地进行SAR图像道路目标自动提取,提出了一种基于遗传算法的SAR图像道路目标自动提取方法。该方法首先通过Frost滤波器去相干斑;然后利用乘性Duda线特征检测算子进行线特征检测,接着利用Radon变换进行线基元提取,再利用遗传算法进行线基元连接;最后利用蛇模型调整道路位置并进行道路鉴别。在星载和机载SAR图像上进行的实验以及性能定量评估结果证明了该方法的有效性。     

SAR有源编码辐射定标技术研究

在对SAR有源编码辐射定标技术的研究中,首先开展了地物杂波信号对SAR辐射定标精度影响的研究,推出了受地物杂波信号影响的系统总体传递函数的误差公式,其次分析了有源编码辐射定标技术抑制地物杂波的机理和信号处理流程。在不同地物杂波分布情况下,完成了SAR有源编码辐射定标技术的仿真实验。实验结果证明SAR有源编码定标技术能够有效地抑制地物杂波信号的影响,改善SAR辐射定标的精度,特别是提高了定标场地选择的灵活性。     

基于灰色关联分析和IHS变换的图像融合算法*

针对IHS融合算法的光谱畸变问题,提出一种基于灰色关联分析和IHS变换的图像融合算法.该算法利用灰色关联分析法检测出SAR图像像素中的边缘点和非边缘点;通过IHS变换,在线性加权融合时赋予边缘点较大的权值.实验表明,该算法在保持光谱信息方面具有显著的优越性,并且能够有效提高融合图像的空间分辨能力,具有简洁性和抗噪性,明显优于传统的IHS融合算法和改进的基于直方图匹配的IHS融合算法.     

RADARSAT卫星产品

RADARSAT卫星所获得的数据是全球应用很广泛的SAR数据信息源之一。本文介绍了该卫星的基本参数及其产品的特点,用图像处理软件打开其产品的方法,以及RADARSAT产品的辐射定标算法。     

区域GMM聚类的SAR图像分割

高斯混合模型(GMM)聚类算法近年来广泛应用于图像分割领域。但在SAR图像分割中,由于忽略了图像像素间的空间相关性,使其对相干斑噪声十分敏感。提出一种基于区域的GMM聚类算法,它将空间相关性引入聚类分类中,利用分水岭分割得到基本同质区域,计算区域的灰度均值作为GMM聚类算法的输入样本,将聚类特征从像素水平提升到区域水平,减少了噪声对分割结果的影响;并将自身反馈机制引入期望最大化(EM)算法中,进一步提高了GMM模型参数估计的精度。还对合成图像和真实SAR图像进行了分割实验,结果表明新算法可有效地提高分割的     

Generation of geometrically and radiometrically terrain corrected SAR image products

Terrain undulations affect the geometric and radiometric quality of synthetic aperture radar images. The correction of these effects becomes indispensable when quantitative image analysis is performed with respect to the derivation of geo- and biophysical parameters. The paper presents a rigorous approach for geometric and radiometric correction of SAR images. Using a digital elevation model, the imaging geometry is reconstructed and is used to perform geometric and radiometric correction of terrain induced distortions. The importance of a stringent radiometric correction based on the integration of the image brightness is emphasized. The approach guarantees that the energy contained in the image data is preserved throughout the geocoding process. The resulting backscattering images are fully terrain corrected and can be used for further quantitative investigations and may also improve qualitative studies as e.g. land cover classifications. The technique is applicable for different sensor types and image products, including already geocoded SAR images. The effect of different resolutions of digital elevation models used for the correction of the backscattering coefficient is investigated.     

SAR图像幅度关联去斑算法

在合成孔径雷达（Synthetic Aperture Radar,SAR）图像相干斑抑制过程中,如何在去斑的同时保持图像的边缘和结构信息是一个难点问题。针对此问题提出了SAR图像幅度关联去斑算法,该算法基于幅度关联,通过全局幅度关联阈值将滑窗内像素分成两个部分,选取像素数目较多的一部分使用最大似然估计方法估计出中心像素的真实值。提出了一种基于频谱统计相似度的评估方法,并利用该评估方法及一些经典的去斑性能评估方法,分别通过仿真数据和实测数据处理结果验证了所提去斑算法的有效性。     

A line-preserving post-processing technique for maximum likelihood classification of SAR images

Speckle in Synthetic Aperture Radar (SAR) images causes many pixels to be misclassified when a maximum likelihood classifier (MLC) is applied. We present a post-processing procedure for removing these misclassified pixels. The procedure, requiring only a few comparisons for each pixel, first detects misclassified pixels, then reclassifies them to the classes of their neighbouring pixels. Experimental results show that this procedure efficiently reduces misclassified pixels while preserving lines, boundaries and corners in thematic maps.     

Boosting: a classification method for remote sensing

This article sets out to demonstrate how boosting can serve as a supervised classification method, and to compare its results with those of conventional methods. The comparison begins with a theoretical example in which several criteria are varied: number of pixels per class, overlapping (or not) of radiometric values between classes, and presence and absence of spatial structuring of classes within the geographical space. The results are then compared with a real case study of land cover based on a multispectral SPOT image of the Sousson catchment area (South of France). It is seen that (1) maximum likelihood gives better results than boosting when the radiometric values for each class are clearly separated. This advantage is lost as the number of pixels per class increases; (2) boosting is systematically better than maximum likelihood in the event of overlapping radiometric variable classes, whether or not there is a spatial structure.     

全极化合成孔径雷达影像地形纠正及其在雪冰制图中的应用

针对合成孔径雷达（SAR）影像由于地形起伏引起的图像畸变问题,文章提出了基于相干矩阵的全极化SAR影像地形纠正算法,并运用于雪冰制图。该方法首先采用距离多普勒模型建立SAR成像几何模型;然后利用全极化Cloude特征分解方法对全极化SAR图像进行融合,将融合后的SAR图像与模拟图像进行配准提高SAR影像几何定位精度;最后利用投影面积归一化和极化方位角移动补偿技术对地形引起的辐射畸变进行纠正。采用中国长江源区南部唐古拉山中段冬克玛底冰川区域的C波段Radarsat-2全极化SAR数据进行验证,配准模拟SAR和原始SAR影像的控制点方位向和距离向的均方根误差（RMSE）分别为7.765和14.586个像素;经过地形纠正后的地物分类精度达80%以上。结果表明：（1）该方法能够有效消除SAR影像中几何和辐射畸变的影响;（2）地形纠正后的SAR数据在雪冰制图中具有可行性。