Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can be determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented.     

基于分段的非监督SAR图像变化检测

本文提出一种基于分段的变化检测方法,该方法首先实现全局最优的图像分段,再根据位置信息产生最佳的变化检测分段图像,最后根据图像灰度和纹理信息对图像进行变化检测,利用合成孔径雷达(SAR)图像进行的实验表明了该方法的有效性.     

基于小波域隐马尔可夫混合模型的SAR图像降斑算法

在小波域马尔可夫随机场(MRF)和隐马尔可夫树(HMT)的基础上,提出了一种新的合成孔径雷达(SAR)图像降斑算法.该算法在对乘性噪声不取对数变换的情况下,融合了贝叶斯最小均方误差(MMSE)抑制噪声技术.为了提高HMT的速度,采用了一个新的隐马尔可夫半树模型,该模型考虑了小波系数的持续性和聚类性,分别用HMT和MRF刻画.仿真结果表明该算法在抑制斑点噪声的同时,有效的保持了边缘,避免对数变换带来的一些误差,取得了好的效果,其速度比HMT模型提高了二十倍.     

Unsupervised texture segmentation using multichannel decompositionand hidden Markov models

In this paper, we describe an automatic unsupervised texture segmentation scheme using hidden Markov models (HMMs). First, the feature map of the image is formed using Laws' micromasks and directional macromasks. Each pixel in the feature map is represented by a sequence of 4-D feature vectors. The feature sequences belonging to the same texture are modeled as an HMM. Thus, if there are M different textures present in an image, there are M distinct HMMs to be found and trained. Consequently, the unsupervised texture segmentation problem becomes an HMM-based problem, where the appropriate number of HMMs, the associated model parameters, and the discrimination among the HMMs become the foci of our scheme. A two-stage segmentation procedure is used. First, coarse segmentation is used to obtain the approximate number of HMMs and their associated model parameters. Then, fine segmentation is used to accurately estimate the number of HMMs and the model parameters. In these two stages, the critical task of merging the similar HMMs is accomplished by comparing the discrimination information (DI) between the two HMMs against a threshold computed from the distribution of all DI's. A postprocessing stage of multiscale majority filtering is used to further enhance the segmented result. The proposed scheme is highly suitable for pipeline/parallel implementation. Detailed experimental results are reported. These results indicate that the present scheme compares favorably with respect to other successful schemes reported in the literature.     

Hybrid approach to speech recognition using hidden Markov modelsand Markov chains

The paper presents a hybrid of a hidden Markov model and a Markov chain model for speech recognition. In this hybrid, the hidden Markov model is concerned with the time-varying property of spectral features, while the Markov chain accounts for the interdependence of spectral features. The log-likelihood scores of the two models, with respect to a given utterance, are combined by a postprocessor to yield a combined log-likelihood score for word classification. Experiments on speaker-independent and multispeaker isolated English alphabet recognition show that the hybrid outperformed both the hidden Markov model and the Markov chain model in terms of recognition     

Unsupervised frequency tracking beyond the Nyquist frequency using Markov chains

This paper deals with the estimation of a sequence of frequencies from a corresponding sequence of signals. This problem arises in fields such as Doppler imaging, where its specificity is twofold. First, only short noisy data records are available (typically four sample long), and experimental constraints may cause spectral aliasing so that measurements provide unreliable, ambiguous information. Second, the frequency sequence is smooth. Here, this information is accounted for by a Markov model, and application of the Bayes rule yields the a posteriori density. The maximum a posteriori is computed by a combination of Viterbi and descent procedures. One of the major features of the method is that it is entirely unsupervised. Adjusting the hyperparameters that balance data-based and prior-based information is done automatically by maximum likelihood (ML) using an expectation-maximization (EM)-based gradient algorithm. We compared the proposed estimate to a reference one and found that it performed better: variance was greatly reduced, and tracking was correct, even beyond the Nyquist frequency.     

Unsupervised segmentation of hidden semi-Markov non-stationary chains

The Bayesian segmentation using Hidden Markov Chains (HMC) is widely used in various domains such as speech recognition, acoustics, biosciences, climatology, text recognition, automatic translation and image processing. On the one hand, hidden semi-Markov chains (HSMC), which extend HMC, have turned out to be of interest in many situations and have improved HMC-based results. On the other hand, the case of non-stationary data can pose an important problem in real-life situations, especially when the model parameters have to be estimated. The aim of this paper is to consider these two extensions simultaneously: we propose using a particular triplet Markov chain (TMC) to deal with non-stationary hidden semi-Markov chains. In addition, we consider a recent particular HSMC having the same computation complexity as the classical HMC. We propose a related parameter estimation method and the resulting unsupervised Bayesian segmentation is validated through experiments; in particular, a real radar image segmentations are provided.     

Multi-aspect target detection for SAR imagery using hidden Markovmodels

Radar scattering from an illuminated object is often highly dependent on the target-sensor orientation. In typical synthetic aperture radar (SAR) imagery, the information in the multi-aspect target signatures is diffused in the image-formation process. In an effort to exploit the aspect dependence of the target signature, the authors employ a sequence of directional filters to the SAR imagery, thereby generating a sequence of subaperture images that recover the directional dependence of the target scattering. The scattering statistics are then used to design a hidden Markov model (HMM), wherein the orientation-dependent scattering statistics are exploited explicitly. This approach fuses information embodied in the orientation-dependent target signature under the assumption that. Both the target identity and orientation are unknown. Performance is assessed by considering the detection of tactical targets concealed in foliage, using measured foliage-penetrating (FOPEN) SAR data     

基于NSCT域图像融合改进算法的SAR图像无监督变化检测

为了降低合成孔径雷达(SAR)遥感图像变化检测总 误差数以及获取更好的变化检测结果,提出了一种基于非下采样轮廓变换(NSCT)域图像融合 改进算法的SAR图像无监督变化检测方法。首先用两时相遥感图像构造得到对数比值 和均值比值两种差异图,并 经过NSCT分解得到各自的高低频系数;然后对两差异图中的低频系数均采用最大梯度平方和 规则进行融合,高频 系数均采用加权规则进行融合,再利用逆NSCT融合得到最终差异图;最后用模糊局部信息C 均值聚类(FLICMC)算法实现对图像变化区 域与非变化区域的分类。通过对真实遥感数据集与模拟遥感数据集的研究表明,本文算法能 够有效抑制斑点噪声对 检测结果的影响,并且不受变化类和非变化类统计分布的限制,不需要先验知识,适用性强 ,可以得到较好的检测 效果。与DWT2-FLICMC、MRF-FCMC算法相比,本文算法具有更高的检测精度和较少的算法 运行时间。     

LMS algorithms for tracking slow Markov chains with applications to hidden Markov estimation and adaptive multiuser detection

This paper analyzes the tracking properties of the least mean squares (LMS) algorithm when the underlying parameter evolves according to a finite-state Markov chain with infrequent jumps. First, using perturbed Liapunov function methods, mean-square error estimates are obtained for the tracking error. Then using recent results on two-time-scale Markov chains, mean ordinary differential equation and diffusion approximation results are obtained. It is shown that a sequence of the centered tracking errors converges to an ordinary differential equation. Moreover, a suitably scaled sequence of the tracking errors converges weakly to a diffusion process. It is also shown that iterate averaging of the tracking algorithm results in optimal asymptotic convergence rate in an appropriate sense. Two application examples, analysis of the performance of an adaptive multiuser detection algorithm in a direct-sequence code-division multiple-access (DS/CDMA) system, and tracking analysis of the state of a hidden Markov model (HMM) with infrequent jumps, are presented.     

基于复Wishart分布的极化SAR图像无监督分类

文中使用了一种无监督算法对全极化合成孔径雷达数据进行地物分类。不同于其他算法对像素统计特性分类而忽略他们的散射特性,这种算法不仅使用了统计分类,而且还保留了其主要的散射特性。本算法采用的是由Freeman和Durden以散射模型为基础开发的分解算法和基于复Wishart分布的距离度量分类器的组合。首先是应用Freeman和Durden分解划分像素分成三个散射类:表面散射,体散射和二面角散射;然后在此基础上将这三个散射类分为多个聚类,通过迭代Wishart分类器将其更精准地分类;最后,根据散射类别的自然颜色对其进行像素编码,提出颜色填充的方案。通过实验结果比对来证明该方法比H/α方法更具有优越性。     

Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering

Five clustering techniques are compared by classifying a polarimetric synthetic aperture radar image. The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard /spl lscr//sub 1/ and /spl lscr//sub 2/ metrics. Two others are new, combining a robust fuzzy C-means clustering technique with a distance measure based on the Wishart distribution. The fifth clustering technique is an application of the expectation-maximization algorithm assuming the data are Wishart. The clustering algorithms that are based on the Wishart are demonstrably more effective than the clustering algorithms that appeal only to the /spl lscr//sub p/ norms. The results support the conclusion that the pixel model is more important than the clustering mechanism.     

Adaptive ship detection in SAR images using variance WIE-based method

Automatic detection of ship targets from synthetic aperture radar (SAR) images is an important and challenging problem. Given the different nature of target returns in homogeneous and heterogeneous regions in SAR imagery, conventional detection algorithms fail to yield automatic and robust results. In support of automatic vessel monitoring, an adaptive detection framework designed for detecting ships from SAR imagery is proposed in this paper, and the variance weighted information entropy is introduced into the framework construction. Experimental results indicate that the proposed method can effectively detect the ship targets from various circumstances without any prior knowledge.     

Road detection in spaceborne SAR images using a genetic algorithm

This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). Roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, the authors designed a grouping method based on a GA, which is a global optimization method. They combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. The authors applied their method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach     

Control theoretic approach to intrusion detection using a distributed hidden Markov model

Cooperative ad hoc wireless networks are more vulnerable to malicious attacks than traditional wired networks. Many of these attacks are silent in nature and cannot be detected by conventional intrusion detection methods such as traffic monitoring, port scanning, or protocol violations. These sophisticated attacks operate under the threshold boundaries during an intrusion attempt and can only be identified by profiling the complete system activity in relation to normal behavior. In this article we discuss a control- theoretic hidden Markov modelstrategy for intrusion detection using distributed observation across multiple nodes. This model comprises a distributed HMM engine that executes in a randomly selected monitor node and functions as a part of the feedback control engine. This drives the defensive response based on hysteresis to reduce the frequency of false positives, thereby avoiding inappropriate ad hoc responses.     

Nonlinear filtering of statistically connected video sequences based on hidden Markov chains

A method for the synthesis of algorithms for nonlinear filtering of statistically connected (correlated) video sequences of digital halftone images distorted by a white Gaussian noise is proposed. The method is based on the use of hidden Markov chains as a mathematical model for video sequences of digital halftone images, which, in limited observation intervals, can be represented by multidimensional Markov chains.     

Change detection on Alaska's North Slope using repeat-pass ERS-1SAR images

Measurements of the C-band (wavelength=5 cm) radar cross section of an area in the Brooks Range foothills on the North Slope of Alaska using images from the ERS-1 satellite that show significant temporal changes occur. These changes are strongly correlated with elevation and hill-slope orientation and are greatest on some of the elevated areas and weaker in river drainages. By constructing difference images using various image pairs, and by analyzing climatological and hydrological data from the site, it is concluded that the radar backscatter changes are largely due to changes in soil and vegetation liquid water content induced by freeze/thaw events. The correlation with topography in the difference images arises from the dependence of vegetation, organic layer thickness, and volumetric water content on hill-slope position and orientation. These results demonstrate the viability of radar backscatter intensity comparisons using repeat-pass images as a means of change detection     

一种基于图像分布的SAR图像边缘检测方法

针对合成孔径雷达图像边缘检测,引入了信号幅一频域上的统计多分辨率分析的概念,结合了非极值抑制和双阈值连接两种处理方法,提出一种新的边缘检测算法.实验结果表明,与传统算法比较,新的边缘检测方法显著提高了边缘检测正确率和边缘定位精度.     

Unsupervised target detection in hyperspectral images usingprojection pursuit

The authors present a projection pursuit (PP) approach to target detection. Unlike most of developed target detection algorithms that require statistical models such as linear mixture, the proposed PP is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest. It utilizes a projection index to explore projections of interestingness. For target detection applications in hyperspectral imagery, an interesting structure of an image scene is the one caused by man-made targets in a large unknown background. Such targets can be viewed as anomalies in an image scene due to the fact that their size is relatively small compared to their background surroundings. As a result, detecting small targets in an unknown image scene is reduced to finding the outliers of background distributions. It is known that “skewness,” is defined by normalized third moment of the sample distribution, measures the asymmetry of the distribution and “kurtosis” is defined by normalized fourth moment of the sample distribution measures the flatness of the distribution. They both are susceptible to outliers. So, using skewness and kurtosis as a base to design a projection index may be effective for target detection. In order to find an optimal projection index, an evolutionary algorithm is also developed to avoid trapping local optima. The hyperspectral image experiments show that the proposed PP method provides an effective means for target detection     

Change detection in multisensor SAR images using bivariate gamma distributions.

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of this paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins, and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors.