Lognormal Random-Field Models and Their Applications to Radar Image Synthesis

Lognormal random fields with multiplicative spatial interaction are proposed for modeling radar image intensity. Two particular classes of two-dimensional (2-D) lognormal random fields are introduced: multiplicative autoregressive (MAR), and multiplicative Mnrkov random fields (MMRF). The MAR and MMRF models are formulated as invertible point-transformations of Gaussian autoregressive and Gaussian Markov random fields (GMRF) and therefore possess many desirable properties. Least squares and maximum likelihood estimates for random-field parameters are presented, a decision rule is developed for selecting model order and transformations to normality as well, and techniques for synthesizing 2-D lognormal random fields are provided. Several Seasat synthetic aperture radar (SAR) images were tested using the decision rule developed in this paper and using the Kolmogorov-Smirnov (K-S) goodness-of-fit test. With both tests they were found to possess a good fit to lognormal statistics. MAR and MMRF models were fit to Seasat SAR images, and then the models were used to generate synthetic images that closely resemble the original SAR images both visually and in their variograms. This demonstrates the generality and appropriateness of the MAR and MMRF models for radar imagery.     

Modeling SAR images with a generalization of the Rayleigh distribution

Synthetic aperture radar (SAR) imagery has found important applications due to its clear advantages over optical satellite imagery one of them being able to operate in various weather conditions. However, due to the physics of the radar imaging process, SAR images contain unwanted artifacts in the form of a granular look which is called speckle. The assumptions of the classical SAR image generation model lead to a Rayleigh distribution model for the histogram of the SAR image. However, some experimental data such as images of urban areas show impulsive characteristics that correspond to underlying heavy-tailed distributions, which are clearly non-Rayleigh. Some alternative distributions have been suggested such as the Weibull, log-normal, and the k-distribution which had success in varying degrees depending on the application. Recently, an alternative model namely the alpha-stable distribution has been suggested for modeling radar clutter. In this paper, we show that the amplitude distribution of the complex wave, the real and the imaginery components of which are assumed to be distributed by the alpha-stable distribution, is a generalization of the Rayleigh distribution. We demonstrate that the amplitude distribution is a mixture of Rayleighs as is the k-distribution in accordance with earlier work on modeling SAR images which showed that almost all successful SAR image models could be expressed as mixtures of Rayleighs. We also present parameter estimation techniques based on negative order moments for the new model. Finally, we test the performance of the model on urban images and compare with other models such as Rayleigh, Weibull, and the k-distribution.     

Geometric accuracy of Ikonos: zoom in

Pioneering in the world, the high-resolution satellite Ikonos imagery has revolutionary changed the market - users now have the unique opportunity to get satellite images with spatial resolution comparable to middle-scale aerial photos. In our study, we tried to find an answer why such images, having submetre spatial resolution, still contain planimetric errors measured in dozens and even hundreds of metres. This paper is aimed at to not only show to potential users of high-resolution satellite images the main sources of image geometric distortions, but to uncover the errors, hardly to be corrected without possessing precise information about particular conditions on the spot at the moment of image acquisition, and evaluate sensibility of the corresponding image correction models for variations of such data.     

Terrain elevation mapping results from airborne spotlight-mode coherent cross-track SAR stereo

Coherent cross-track synthetic aperture radar (SAR) stereo is shown to produce high-resolution three-dimensional maps of the Earth surface. This mode utilizes image pairs with common synthetic apertures but different squint angles allowing automated stereo correspondence and disparity estimation using complex correlation calculations. This paper presents two Ku-band, coherent cross-track stereo collects over rolling and rugged terrain. The first collect generates a digital elevation map (DEM) with 1-m posts over rolling terrain using complex SAR imagery with spatial resolution of 0.125 m and a stereo convergence angle of 13.8/spl deg/. The second collect produces multiple DEMs with 3-m posts over rugged terrain utilizing complex SAR imagery with spatial resolutions better than 0.5 m and stereo convergence angles greater than 40/spl deg/. The resulting DEMs are compared to ground-truth DEMs and relative height root-mean-square, linear error 90-percent confidence, and maximum height error are reported.     

Wind vector retrieval using ERS-1 synthetic aperture radar imagery

An automated algorithm intended for operational use is developed and tested for estimating wind speed and direction using ERS-1 SAR imagery. The wind direction comes from the orientation of low frequency, linear signatures in the SAR imagery that the authors believe are manifestations of roll vortices within the planetary boundary layer. The wind direction thus has inherently a 180° ambiguity since only a single SAR image is used. Wind speed is estimated by using a new algorithm that utilizes both the estimated wind direction and σ ₀ values to invert radar cross section models. The authors show that: 1) on average the direction of the roll vortices signatures is approximately 11° to the right of the surface wind direction and can be used to estimate the surface wind direction to within ±19° and 2) utilizing these estimated wind directions from the SAR imagery subsequently improves wind speed estimation, generating errors of approximately ±1.2 m/s, for ERS-1 SAR data collected during the Norwegian Continental Shelf Experiment in 1991     

基于地面激光的探地雷达图像地形校正方法

为消除地形起伏对探地雷达图像畸变和解译的影响,需进行地形校正处理。本文主要通过地面激光建立的数字高程模型(Digital Elevation Model,DEM)来实现探地雷达图像的地形校正。首先,利用激光点云构建高精度DEM,借助靶球标记的雷达测线起始和结束位置实现雷达图像与DEM上提取出相应的二维地形剖面之间的匹配。然后,选择测线上最高点所在的水平面为参考基准面,根据时间移位原理并结合线性插值法对雷达图像上各道数据的双程传播时间进行校正;最后,选择实例数据并以差分GPS采集的地形数据为标准,着重分析了不同分辨率DEM下提取出的地形剖面对地形校正效果的影响。结果表明利用激光点云建立DEM实现探地雷达图像地形校正的有效性,当DEM分辨率与探地雷达数据采集道间距相一致时,从DEM上提取出的地形剖面实现探地雷达图像地形校正的效果最佳。     

Terrain influences on SAR backscatter around Mt. Taranaki, NewZealand

An L-band SAR image from the Japanese JERS-1 satellite has been analyzed for the effects of local surface orientation relative to the radar illumination direction. Orthorectification of the SAR imagery and determination of the local surface orientation is achieved with the aid of a high resolution digital terrain model. An improved method of determining scatterer density for a surface in three dimensions is introduced and used to correct radiometrically the image for terrain variation. Residual radiometric effects due to surface orientation are shown to be dependent on the ground cover class. Backscatter from the indigenous forest was more isotropic than that from the farmland. As accurate registration was required for this study, a method for identifying control points in the rectified imagery is described which alleviated the difficulty of identifying them in the raw image     

IECAS高分辨率机载合成孔径雷达几何精度试验

本文以实际地形图数据,用经典的雷达摄影测量的方法,检验了中国科学院电子学研究所(IECAS)高分辨率机载合成孔径雷达的几何精度。结果表明:该雷达影像在单图像修测(对已有地图进行更新测量)条件下,平面位置精度优于4m米;在立体图像量测的条件下, 空间位置精度优于20m。     

Multiscale segmentation and anomaly enhancement of SAR imagery

We present efficient multiscale approaches to the segmentation of natural clutter, specifically grass and forest, and to the enhancement of anomalies in synthetic aperture radar (SAR) imagery. The methods we propose exploit the coherent nature of SAR sensors. In particular, they take advantage of the characteristic statistical differences in imagery of different terrain types, as a function of scale, due to radar speckle. We employ a class of multiscale stochastic processes that provide a powerful framework for describing random processes and fields that evolve in scale. We build models representative of each category of terrain of interest (i.e., grass and forest) and employ them in directing decisions on pixel classification, segmentation, and anomalous behaviour. The scale-autoregressive nature of our models allows extremely efficient calculation of likelihoods for different terrain classifications over windows of SAR imagery. We subsequently use these likelihoods as the basis for both image pixel classification and grass-forest boundary estimation. In addition, anomaly enhancement is possible with minimal additional computation. Specifically, the residuals produced by our models in predicting SAR imagery from coarser scale images are theoretically uncorrelated. As a result, potentially anomalous pixels and regions are enhanced and pinpointed by noting regions whose residuals display a high level of correlation throughout scale. We evaluate the performance of our techniques through testing on 0.3-m resolution SAR data gathered with Lincoln Laboratory's millimeter-wave SAR.     

高分辨SAR目标散射中心模型分析

散射中心是光学区雷达目标电磁散射的基本特征,是高分辨SAR图像解译的根本特征。以高分辨SAR图像解译为应用背景,分析了目前典型的三种散射中心模型（理想点散射中心模型、衰减指数和模型、属性散射中心模型）的优缺．点和适用条件,并导出了模型及模型参数间的关系。通过理论分析、仿真及实测SAR图像数据验证了属性散射中心模型是目前最符合高分辨SAR图像解译应用需求的散射中心模型。在此基础上,分析了属性散射中心的特性与特点,为研究基于属性散射中心模型的高分辨SAR图像特征提取及解译方法奠定了基础。     

基于RADARSAT-2数据的SAR图像双视向几何校正方法研究

针对山区高分辨率雷达卫星Radarsat-2图像的严重几何变形,提出了一种新的双视向雷达图像几何校正方法.实验证明该方法可以有效祛除地形引起的各种几何变形,防止地形引起的后向散射系数的失真,特别是能够有效祛除叠掩和阴影的影响,而这个问题是基于单幅雷达图像的传统几何校正方法无法解决的.为合成孔径雷达(SAR)图像在山区的...     

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.     

Removal of terrain effects from SAR satellite imagery of Arctictundra

Synthetic aperture radar (SAR) images of the Earth's terrestrial surface contain geometric and radiometric image effects which are caused by varying terrain elevation and slope. The radiometric effects tend to mask signal variations caused by other physical variables such as soil moisture and surface vegetation type, which are known to influence SAR backscatter signals. As a result, raw SAR images are of limited use in classifying surface vegetation type or quantifying the spatial distribution of soil moisture in regions of terrain relief, The authors present a technique for removing radiometric terrain effects from SAR images. Image correction was carried out in two steps. First, an existing modeling package was used in combination with digital elevation data in order to map the raw image pixels onto a geodetic coordinate system, thereby removing the geometric portion of the image distortion. Radiometric effects were then removed with the aid of a backscatter model which treats the reflected radiation as a combination of diffuse-Lambertian and specular components. Parameters in the backscatter model were determined by comparing two C-band SAR images of a test area in a region of Arctic tundra which were taken from ascending and descending orbit tracks of the ERS-1 satellite. The ascending and descending images displayed reductions in pixel value variance of 30% and 13%, respectively, after processing. Direct comparison of the two test area images reveals a dramatic improvement in image similarity after processing     

一种机载SAR快速几何精校正算法

几何定位精度是SAR在遥感测绘领域的一个重要技术指标。机载SAR具备高机动性、高分辨率、低成本等方面的优势,是SAR技术发展的一个重要方向。运动误差和地形起伏是机载SAR几何定位的重要误差来源。该文从SAR成像原理和成像几何的角度出发,深入研究了运动误差与地形起伏耦合下几何定位误差的产生机理,并在此基础上提出了一种快速几何精校正方法。仿真实验和实测数据结果验证了该方法的正确性和有效性。      

Resolution of the ocean wave propagation direction in SAR imagery

There is an inherent 180° ambiguity in the derived wave propagation direction when using conventional spectral analysis techniques on standard synthetic aperture radar (SAR) image products. Three different techniques are successfully used to resolve this ambiguity in propagation direction using a single pass of airborne SAR data. The fact that the SAR is characterized by a large time-bandwidth product is used to advantage. A sequence of individual looks extracted from the Doppler spectrum represents images of the scene collected at a sequence of discretely delayed intervals of time. The techniques utilized include cross-correlation-based motion analysis of a pair of looks, phase weighting based upon a pair of looks and the ocean wave dispersion relation, and a three-dimensional spectral analysis. The phase weighting technique is also demonstrated for a Seasat SAR scene     

星载SAR图像几何校正影响要素分析

几何校正是SAR图像应用的基础,而影响校正效果的要素有多种。论文首先从SAR图像成像机理出发,阐述了几何校正中定位模型的建立和解算过程,主要分析了两类影响要素：高程和卫星轨道插值方法。然后以一幅高分辨率TerraSAR-X图像的几何校正为基础,对比分析了三种高程和两种轨道插值算法对定位精度的影响。实验结果表明,只有距离向定位受这些因素的影响,高程的分辨率和垂直精度对定位精度都有很大影响、四次多项式直接插值方法只能满足一般的几何校正需要,更高精度的校正还需要其他辅助手段。     

Sea Ice Deformation State From Synthetic Aperture Radar Imagery—Part II: Effects of Spatial Resolution and Noise Level

C- and L-band airborne synthetic aperture radar (SAR) imagery acquired at like- and cross-polarizations over sea ice under winter conditions is examined with the objective to study the discrimination between level ice and ice deformation features. High-resolution low-noise data were analyzed in the first paper. In this second paper, the main topics are the effects of spatial resolution and signal-to-noise ratio. Airborne high-resolution SAR scenes are used to generate a sequence of images with increasingly coarser spatial resolution from 5 to 25 m, keeping the number of looks constant. The signal-to-noise ratio is varied between typical noise levels for airborne imagery and satellite data. Areal fraction of deformed ice and average deformation distance are determined for each image product. At L-band, the retrieved values of the areal fraction get larger as the image resolution is degraded. The areal fraction at C-band remains constant. The retrieved average distance between deformation features increases both at C- and L-bands as the image resolution gets coarser. The influence of noise becomes noticeable if its level is equal or larger than the average intensity backscattered from the level ice. The retrieval of deformation parameters using simulated images that resemble ERS-2 SAR, Envisat ASAR, and ALOS PALSAR data products is discussed. Basic differences between real and simulated ERS-2 SAR images are analyzed.      

Textural Infornation in SAR Images

A multiplicative model was used to relate the image variance for a given land-use category to the individual variances associated with image speckle and target texture. Speckle was treated as a random process governed by signal fading and was considered to be statistically independent of the textural variations associated with the spatial variations of the scattering properties of visually "uniform" distributed targets. Seasat SAR imagery of Oklahoma was used to evaluate the textural autocorrelation function of five land-use categories: water, forest, pasture, urban, and cultivated. It was found that the maximum classification accuracy achievable using first-order statistics was 72 percent and that this level of accuracy was obtainable only by significantly degrading the spatial resolution in order to increase the number of independent samples per pixel. In contrast, second-order statistics-specifically, image contrast and inverse moment-provided a classification accuracy of 88 percent, with only a modest degradation in spatial resolution. A second study using SIR-A imagery of five forested regions has shown that the use of textural information can improve the classification accuracy among the five forest types from 75 to 93 percent.     

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm, where each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements that it provides over existing techniques for model error compensation in SAR.     

基于多源遥感影像的多尺度城市植被覆盖度估算

以Landsat 7 ETM+、SPOT 5和IKONOS遥感影像数据为数据源,利用格网法从1∶500地形图提取的不同空间分辨率的植被覆盖度为参考依据,通过对不同辐射校正水平的遥感影像获得的植被覆盖度进行精度比较分析,对多源多尺度和多源同尺度城市植被覆盖度估算的相关问题进行研究.研究表明,在城市区域进行植被覆盖度估算时,ICM模型为较佳辐射校正模型;对于高分辨遥感影像,NDVI为植被覆盖度估算的较佳植被指数;对于中分辨率影像,植被覆盖度估算的较佳植被指数则为RVI和MSAVI;就研究区而言GI模型比CR模型估算的植被覆盖度更准确.