A FARIMA-based technique for oil slick and low-wind areas discrimination in sea SAR imagery

This paper introduces a new analysis technique, using the fractionally integrated autoregressive-moving average (FARIMA) model, to distinguish between low-wind and oil slick areas in high-resolution sea synthetic aperture radar (SAR) imagery. The method deals with the estimation of the fractional differencing and autoregressive-moving average parameters of the mean radial power spectral density of sea SAR images. The algorithm is applied and validated on dark areas corresponding to oil slicks, oil spills, and low-wind sea surface anomalies in European Remote Sensing 1 and 2 Precision Images of the Mediterranean Sea, North Sea, and Atlantic Ocean.     

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.     

A statistical mechanical analysis of postural sway using non-Gaussian FARIMA stochastic models

In this paper, postural sway is modeled using a fractional autoregressive integrated moving average (FARIMA) family of models: the center-of-pressure (COP) motion is viewed in terms of a self-similar, anti-persistent random-walk process, obtained by fractionally summating non-Gaussian random variables, whose correlation structure for small time lags is shaped by a linear time-invariant low-pass filter. The model parameters are: the strength of the stochastic driving, e.g., the root mean square (rms) value of the time-difference COP motion; the DC gain, damping ratio and natural frequency of the filter; the Hurst exponent, which measures the random-walk antipersistence magnitude. In the proposed modeling procedure, a graphical estimator for determining the Hurst exponent is cascaded to a method for matching autoregressive (AR) models to fractionally difference COP motion via higher order cumulants. The effect of the presence or absence of vision on the model parameter values is discussed with regard to data from experiments on healthy young adults.     

Analytical modelling and software implementation of synthetic aperture radars

Synthetic aperture radar (SAR) imaging systems are a complement to passive remote sensing. However, the process of image formation is so complex that the final image in the system is formed only after three basic steps: raw data acquisition, forming the signal and the image space. In addition, there are various factors that are involved in the information recorded by SAR within the system and outside the system, such as radar, platform, processing algorithm, imaging region and channel. Each of these factors has been formed by many parameters and this issue adds to the complexity of the behaviour of SAR. Therefore, due to the complexity, providing a model that describes how the SAR imaging system works is highly important. In this paper, the performance of SAR in the image formation section is analytically modelled at first, and then implemented as software. Raw data acquisition is performed in CST software and the signal and image formation are performed in MATLAB software. This implementation provides many abilities, such as better interpretation of SAR images, simulating the effect of the important parameters in SAR images, etc.     

A statistical approach for determining radiometric precisions andaccuracies in the calibration of synthetic aperture radar imagery

A model that estimates a relative error bound for the radiometric calibration of synthetic aperture radar (SAR) imagery is presented. This model is based on a statistical `Coefficient of Variation of Error Model', which produces a relative error bound by propagating the measured or estimated uncertainties in the radar system parameters utilized to correct digitally processed SAR image intensity values. Using this model, algorithms are generated for absolute and relative radiometric calibration of SAR imagery. These algorithms are parametrically exercised using radar system parameters from an existing airborne SAR system to determine their impact on the relative error bound     

Noncooperative target classification using hierarchical modeling ofhigh-range resolution radar signatures

The classification of high-range resolution (HRR) radar signatures using multiscale features is considered. We present a hierarchical autoregressive moving average (ARMA) model for modeling HRR radar signals at multiple scales and use spectral features extracted from the model for classifying radar signatures. First, we show that the radar signal at a different scale obeys an ARMA process if it is an ARMA process at the observed scale. Then, an algorithm to estimate model parameters and power spectral density function at different scales using model parameters at the observed scale is presented. A feature set composed of spectral peaks is extracted from the estimated spectral density function using multiscale ARMA models. For HRR radar signature classification, multispectral features extracted from five different scales are used, and a minimum distance classifier with multiple prototypes is used to classify HRR data. The multiscale classifier is applied to two HRR radar data sets. Each data set contains 2500 test samples and 2500 training samples in five classes. For both data sets, about 95% of the radar returns are correctly classified     

Two-Dimensional Variation Algorithm for Fractal Analysis of Sea SAR Images

This paper proposes a novel algorithm for estimating the fractal dimension of sea synthetic aperture radar (SAR) images. The algorithm is based on the variation method, and it is suitably designed for the analysis of sea SAR images. The SAR image fractal dimension is a feature that provides a measure of the image roughness. Such a feature can play an important role in the classification process for recognizing the presence of anomalies on the sea surface. The innovation aspects of this paper are listed as follows: 1) an extension of the variation method, which was proposed for the fractal analysis of one-dimensional signals, to the case of two-dimensional (2-D) functions; 2) a numerical formulation of the variation method, which is suitable for processing 2-D discrete signals; and 3) an optimization of the algorithm for sea SAR image analysis. The algorithm is tested and validated both on simulated and real ERS-1/2 Precision Image sea SAR images and compared with the classical estimation algorithm based on spectral analysis.     

Initialization of Markov random field clustering of large remote sensing images

Markov random field (MRF) clustering, utilizing both spectral and spatial interpixel dependency information, often improves classification accuracy for remote sensing images, such as multichannel polarimetric synthetic aperture radar (SAR) images. However, it is heavily sensitive to initial conditions such as the choice of the number of clusters and their parameters. In this paper, an initialization scheme for MRF clustering approaches is suggested for remote sensing images. The proposed method derives suitable initial cluster parameters from a set of homogeneous regions, and estimates the number of clusters using the pseudolikelihood information criterion (PLIC). The method works best for an image consisting of many large homogeneous regions, such as agricultural crops areas. It is illustrated using a well-known polarimetric SAR image of Flevoland in the Netherlands. The experiment shows a superior performance compared to several other methods, such as fuzzy C-means and iterated conditional modes (ICM) clustering.     

An adaptive filtering approach to spectral estimation and SARimaging

We present an adaptive FIR filtering approach, which is referred to as the amplitude and phase estimation of a sinusoid (APES), for complex spectral estimation. We compare the APES algorithm with other FIR filtering approaches including the Welch (1967) and Capon (1969) methods. We also describe how to apply the FIR filtering approaches to target range signature estimation and synthetic aperture radar (SAR) imaging. We show via both numerical and experimental examples that the adaptive FIR filtering approaches such as Capon and APES can yield more accurate spectral estimates with much lower sidelobes and narrower spectral peaks than the FFT method, which is also a special case of the FIR filtering approach. We show that although the APES algorithm yields somewhat wider spectral peaks than the Capon method, the former gives more accurate overall spectral estimates and SAR images than the latter and the FFT method     

On the cross spectrum between individual-look synthetic apertureradar images of ocean waves

Cross spectra of individual-look synthetic aperture radar (SAR) images of the ocean surface are used to retrieve ocean wave spectra. A quasilinear transform is derived that relates ocean wave spectra to SAR image cross spectra. Furthermore, Monte Carlo simulations are also carried out for those cases where quasilinear imaging does not apply. It is shown that, as the time separation between the individual-look SAR images increases (within a limit determined by the Doppler bandwidth of the original single-look complex SAR image), the spectral energy density of the imaginary part of the SAR image cross spectra increases, while the spectral energy density of the real part decreases. The integration time has a small effect on the SAR image cross spectra as long as the integration time is large compared to the scene coherence time. In order to retrieve ocean wave spectra from SAR data by using cross-spectral analysis techniques, the authors suggest calculating two SAR image cross spectra: one with a short time separation and one with a large one between the individual-look SAR images. The real part of the SAR image cross spectra calculated from individual-look SAR images with the short time separation is used for retrieving ocean wave spectra, which have a 180° ambiguity in wave propagation direction. The imaginary part of the SAR image cross spectra calculated from individual-look SAR images with the long time separation is used for removing this 180° ambiguity     

基于混合双隐层径向基函数网络的高分辨率SAR图像地物分类算法研究

本文在高分辨率条件下对传统的合成孔径雷达(SAR)图像自动地物分类技术进行了扩展研究.文章首先指出了经典的前馈神经网络模型在SAR图像地物分类中的不足,然后基于径向基神经网络(RBFN),结合混合专家系统,提出了一种变型的网络结构模型,称之为混合双隐层径向基函数网络(MDHRBFN),并将其应用于高分辨率单视单极化的SAR图像地物分类.实验结果表明,基于该模型的分类算法能够将SAR图像较好地区分为人造目标类、自然目标类、背景和阴影,具有比经典RBFN模型更好的分类效果,不但可以应用于SAR图像辅助判读,而且能够为目标识别过程提供潜在目标切片.     

SAR target recognition using behaviour library of different shapes in different incidence angles and polarisations

Target recognition is one of the most important issues in the interpretation of the synthetic aperture radar (SAR) images. Modelling, analysis, and recognition of the effects of influential parameters in the SAR can provide a better understanding of the SAR imaging systems, and therefore facilitates the interpretation of the produced images. Influential parameters in SAR images can be divided into five general categories of radar, radar platform, channel, imaging region, and processing section, each of which has different physical, structural, hardware, and software sub-parameters with clear roles in the finally formed images. In this paper, for the first time, a behaviour library that includes the effects of polarisation, incidence angle, and shape of targets, as radar and imaging region sub-parameters, in the SAR images are extracted. This library shows that the created pattern for each of cylindrical, conical, and cubic shapes is unique, and due to their unique properties these types of shapes can be recognised in the SAR images. This capability is applied to data acquired with the Canadian RADARSAT1 satellite.     

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.     

应用信息论研究合成孔径雷达成象系统

本文将合成孔径雷达（ＳＡＲ）成象系统视为一个多元接入信息系统，利用信息论分析系统信息的传递，系统平均互信息不仅可以用于度量从雷达图象上提取点目标的信息量，而且还可以用于定义雷达图象的辐射分辨率，在深入分析系统构成的条件下，求出从回波信号的独立取样到输出图象信号间的传递概率密度函数，进而求出系统平均互信息解析表达式，得到一系列结论，研究结果定量地解释和说明了许多已有的结论，并为进一步深入研究打下了基     

一种海洋涡旋SAR图像仿真方法

海洋涡旋对海洋热循环起着关键作用,是海洋科学研究中的一个重要分支。合成孔径雷达(Synthetic Aperture Radar, SAR)为海洋涡旋的观测和研究提供了大量的图像数据,但是涡旋在SAR成像时会受到各种海洋环境因素的影响,难以解译涡旋SAR图像特征。仿真SAR图像可以用于研究涡旋的特征,但是目前极少有关于涡旋SAR图像仿真方法的研究。为了更好地解译SAR图像中的涡旋特征,该文提出了一种涡旋SAR图像仿真方法。首先,基于流体力学中典型的Burgers-Rott涡旋模型,建立涡旋2维表面流场;然后,利用SAR海洋成像仿真模型,仿真给定涡旋2维流场、海面风场以及雷达系统参数下的涡旋SAR图像。该文针对气旋式涡旋与反气旋式涡旋进行了仿真实验,并建立了仿真涡旋SAR图像的相似度评价标准。实验结果表明,仿真的涡旋SAR图像与真实星载涡旋SAR图像能够较好地吻合,验证了方法的有效性。      

Detection of Multiple Changes in Fractional Integrated ARMA Processes

This paper addresses the problem of changepoint detection in FARIMA processes. The received signal is modeled as a FARIMA process, with abrupt changes in the Hurst and ARMA parameters. The proposed changepoint detection method first estimates the model parameters over small segments. The changes are then detected in the parameter vector sequence by minimizing an appropriate least-squares criterion. The cases of known, as well as unknown, number of changes are investigated. Dynamic programming is used to solve this optimization problem. A theoretical analysis of the statistical properties of the change point estimates is provided. Simulation results on synthetic data and real network traffic data are presented.     

A Three-Dimensional Formulation for Synthetic Aperture Radar Images of Ocean Waves in Orbital Motions

An analytic model of the ocean surface SAR images with a three-dimensional framework is developed following the formalism presented by Swift and Wilson (1979); a trochoidal swell propagates through a uniform field of Bragg-type distributed scatterers. Two- dimensional SAR images are calculated for the interpretation and prediction of actual SAR images of the ocean surface as a function of ocean wave amplitude, wave frequency, propagation direction, and radar frequency, off-nadir angle of the antenna, and spatial resolutions.     

基于分数阶傅里叶变换的运动舰船聚焦算法

为解决合成孔径雷达(SAR)图像中运动舰船目标产生的散焦现象,结合对比度最大算法和分数阶傅里叶变换(FRFT)算法,提出了一种改进的对比度分数阶傅里叶变换(CFRFT)自聚焦算法.该算法利用分数阶傅里叶变换对已成像SAR图像进行时频域分析,根据旋转角分别利用参数模型和非参数模型对二阶相位误差和高阶相位误差进行补偿,和传统的相位梯度(PGA)法相比,图像分辨率和旁瓣比提升显著,可以更有效地补偿SAR中舰船运动产生的相位误差.对不同舰船和尾迹SAR图像实验表明,算法对二阶以上的相位误差具有较好的补偿效果,误差估计准确性高,适用范围广,解决了SAR运动舰船的散焦问题,提高了海洋舰船监测的准确性.     

ERS-1 SAR images of atmospheric gravity waves

ERS-1 synthetic aperture radar (SAR) images of atmospheric gravity waves over the ocean are discussed. Several case studies are presented in detail. It is shown that the well-organized long wavelength (1 to 10 km) wave phenomena which often are seen in SAR images over the ocean may be atmospheric gravity waves. The waves appear in the SAR images because they modulate the surface wind speed which in turn modulates the surface roughness and the radar cross section. The wavelength may be measured directly from the SAR image, and the mean wind speed and wind speed modulation near the ocean surface may be estimated from the observed radar cross section modulation using a wind retrieval model. The atmospheric gravity waves usually were generated by the approach or passing of a meteorological front. Atmospheric soundings and a two-layer model for the lower troposphere indicate that, in general, the observed atmospheric wave phenomena could have been supported by accompanying temperature inversion layers and wind shears     

CFAR edge detector for polarimetric SAR images

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data.