An advanced system for the automatic classification of multitemporal SAR images

A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis.     

Iterative algorithms for state estimation of jump Markov linearsystems

Jump Markov linear systems (JMLSs) are linear systems whose parameters evolve with time according to a finite state Markov chain. Given a set of observations, our aim is to estimate the states of the finite state Markov chain and the continuous (in space) states of the linear system. In this paper, we present original deterministic and stochastic iterative algorithms for optimal state estimation of JMLSs. The first stochastic algorithm yields minimum mean square error (MMSE) estimates of the finite state space Markov chain and of the continuous state of the JMLS. A deterministic and a stochastic algorithm are given to obtain the marginal maximum a posteriori (MMAP) sequence estimate of the finite state Markov chain. Finally, a deterministic and a stochastic algorithm are derived to obtain the MMAP sequence estimate of the continuous state of the JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problem of deconvolution of Bernoulli-Gaussian (BG) processes and the problem of tracking a maneuvering target are addressed     

Parameter Identification in Degradation Modeling by Reversible-Jump Markov Chain Monte Carlo

In this work, the reversible-jump Markov chain Monte Carlo technique is applied for identifying the parameters governing stochastic processes of component degradation. Two case studies are examined concerning the evolution of deteriorating systems whose parameters undergo step changes in time. The method turns out to be capable of identifying the instances of change in behavior, and of estimating the parameter values. A Bayesian updating strategy is proposed to refine the parameter estimates as new data are made available.      

Improved Phoneme-Based Myoelectric Speech Recognition

This paper introduces an enhanced phoneme-based myoelectric signal (MES) speech recognition system. The system can recognize new words without retraining the phoneme classifier, which is considered to be the main advantage of phoneme-based speech recognition. It is shown that previous systems experience severe performance degradation when new words are added to a testing dataset. To maintain high accuracy with new words, several improvements are proposed. In the proposed MES speech recognition approach, the raw MES is processed by class-specific rotation matrices to spatially decorrelate the data prior to feature extraction in a preprocessing stage. Then, an uncorrelated linear discriminant analysis is used for dimensionality reduction. The resulting data are classified through a hidden Markov model classifier to obtain the phonemic log likelihoods of the phonemes, which are mapped to corresponding words using a word classifier. An average word classification accuracy of 98.533% is achieved over six subjects. The system offers dramatically improved accuracy when expanding a vocabulary, offering promise for robust large-vocabulary myoelectric speech recognition.     

On the almost sure rate of convergence of linear stochastic approximation algorithms

The almost sure rate of convergence of linear stochastic approximation algorithms is analyzed. As the main result, it is demonstrated that their almost sure rate of convergence is equivalent to the almost sure rate of convergence of the averages of their input data sequences. As opposed to most of the existing results on the rate of convergence of stochastic approximation which cover only algorithms with the noise decomposable as the sum of a martingale difference, vanishing and telescoping sequence, the main results of this paper hold under assumptions not requiring the input data sequences to admit any particular decomposition. Although no decomposition of the input data sequences is required, the results on the almost sure rate of convergence of linear stochastic approximation algorithms obtained in this correspondence are as tight as the rate of convergence in the law of iterated logarithm. Moreover, the main result of this correspondence yields the law of iterated logarithm for linear stochastic approximation if the law of iterated logarithm holds for the input data sequences. The obtained general results are illustrated with two (nontrivial) examples where the input data sequences are strongly mixing strictly stationary random processes or functions of a uniformly ergodic Markov chain. These results are also applied to the analysis of least mean square (LMS) algorithms.     

An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery

The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover changes from space. Satellite observations are carried out regularly and continuously, and provide a great deal of insight into the temporal changes of land cover use. High spatial resolution imagery better resolves the details of these changes and makes it possible to overcome the "mixed-pixel" problem that is inherent with more moderate resolution satellite sensors. At the same time, high-resolution imagery presents a new challenge over other satellite systems, in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify the land cover changes. To obtain the accuracies that are required by many applications to large areas, very extensive manual work is commonly required to remove the classification errors that are introduced by most methods. To improve on this situation, we have developed a new method for land surface change detection that greatly reduces the human effort that is needed to remove the errors that occur with many classification methods that are applied to high-resolution imagery. This change detection algorithm is based on neural networks, and it is able to exploit in parallel both the multiband and the multitemporal data to discriminate between real changes and false alarms. In general, the classification errors are reduced by a factor of 2-3 using our new method over a simple postclassification comparison based on a neural-network classification of the same images.     

A Markov random field model for classification of multisourcesatellite imagery

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery     

A methodology for reliability analysis in health networks

A reliability model for a health care domain based on requirement analysis at the early stage of design of regional health network (RHN) is introduced. RHNs are considered as systems supporting the services provided by health units, hospitals, and the regional authority. Reliability assessment in health care domain constitutes a field-of-quality assessment for RHN. A novel approach for predicting system reliability in the early stage of designing RHN systems is presented in this paper. The uppermost scope is to identify the critical processes of an RHN system prior to its implementation. In the methodology, Unified Modeling Language activity diagrams are used to identify megaprocesses at regional level and the customer behavior model graph (CBMG) to describe the states transitions of the processes. CBMG is annotated with: 1) the reliability of each component state and 2) the transition probabilities between states within the scope of the life cycle of the process. A stochastic reliability model (Markov model) is applied to predict the reliability of the business process as well as to identify the critical states and compare them with other processes to reveal the most critical ones. The ultimate benefit of the applied methodology is the design of more reliable components in an RHN system. The innovation of the approach of reliability modeling lies with the analysis of severity classes of failures and the application of stochastic modeling using discrete-time Markov chain in RHNs.     

Hidden Markov modeling of flat fading channels

Hidden Markov models (HMMs) are a powerful tool for modeling stochastic random processes. They are general enough to model with high accuracy a large variety of processes and are relatively simple allowing us to compute analytically many important parameters of the process which are very difficult to calculate for other models (such as complex Gaussian processes). Another advantage of using HMMs is the existence of powerful algorithms for fitting them to experimental data and approximating other processes. In this paper, we demonstrate that communication channel fading can be accurately modeled by HMMs, and we find closed-form solutions for the probability distribution of fade duration and the number of level crossings     

On Probability Density for Modeling Video Traffic

Accurate models for variable bit rate (VBR) video traffic need to allow for different frame types present in the video, different activity levels for different frames, and a variable group of pictures (GOP) structure. The temporal as well as the stochastic properties of the trace data need to be captured by any models. We propose some models that capture temporal properties of the data using doubly Markov processes and autoregressive models. We highlight the importance of capturing the stochastic properties of the data accurately, as this leads to significant improvement in the performance of the model. In order to capture the stochastic properties of the traces, the probability density function of the trace data needs to be accurately modeled. Hence, the focus of this paper is on creating autoregressive processes with arbitrary probability densities. We relate this to work in wavelet theory on the solutions to two-scale dilation equations. The performance of our model is evaluated in terms of the stochastic properties of the generated trace as well as using network simulations.     

Stochastic Models for Multistage Cell Classification Systems

Probabilistic models for multistage cell classification systems are described. A simple finite Markov chain models classification events which occur as a cell passes through the system. The state space consists of various identities assigned to the cell, including true celi type and identities assigned by classifiers. Effects of throughput rate, data buffer capacity, and classifier processing rate on system performance are predicted by another model composed of a network of single server queues. Markov and queue models are interrelated in that classification events at one processor (modeled by the Markov chain) govern arrival rates of other processors. In turn, the queue model predicts the probability that a cell wili be missed due to fmite data buffer capacity. The miss event is modeled by the Markov chain as a possible classification outcome. Application of the models is illustrated for a multistage gynecologic flow prescreening system with slit-scan processing in the first stage and two dimensional image processing in the second. Results predict system sensitivity as a function of first stage false alann rate and abnormal cell occurrence rate.     

Multitemporal Dimensionality of Images of Normalized Difference Vegetation Index at Continental Scales

Two sets of multitemporal data derived from NOAA world data product are analyzed by means of principal components analysis in order to examine their underlying multitemporal dimensionality. Specifically, images of the normalized difference vegetation index (NDVI) were analyzed for eight 3-week periods for Africa and ten 3-week periods for North America sampled from throughout the year extending from April 1982 to March 1983. The two multitemporal sets of images displayed remarkable similarities in terms of their first two components, the first corresponding very closely to the annualized integrated NDVI and the second to the seasonality of the NDVI. In particular, for the African data set the feature space defined by the first two components allows separation of the main cover types.     

Availability modeling of energy management systems

Energy management system (EMS) computer architectures have changed significantly over the recent past increasing the difficulty and the need for a priori assessment of system performance and dependability. The old practice based on measurements is no longer acceptable because of the flexibility accrued with the deployment of the new distributed computer-based systems. The number of “what if” questions increased since EMS systems are now implemented using multiple workstations that can be interconnected in various different ways.In this paper we show how alternative configurations can be modeled and analyzed, before proposing and purchasing any equipment, with the assistance of Markov reward models. We review the concept of Markov reward models and show how they can be applied in the availability analysis of SCADA/EMS computer systems. The paper also presents a software tool that facilitates automatic generation and solution of large Markov reward models. The input language of this modeling tool uses a variation of stochastic Petri nets called stochastic reward nets, which are also reviewed. We believe this is the first time a detailed quantitative model of a SCADA/EMS computer system is proposed and solved in the general literature.     

Introduction to Cyclic Replacement Systems

A fundamental and unified treatment of problems akin to the classical Swedish Machine Problem is presented. Section I describes the nature of the systems known as cyclic replacement systems. In Section II pertinent facts about Markov processes are gathered. In Section III, it is shown that a certain class of cyclic systems behave as homogeneous Markov processes. The special class of homogeneous Markov processes known as homogeneous birth and death processes is considered in Section IV. Results of Section IV are applied to some cyclic replacement systems in Section V. In Section VI some systems are treated which cannot be represented as birth and death processes.     

一类随机动态过程基于q阶树的多尺度建模方法

利用多尺度随机模型能建立处理问题有效并行算法的这一优势,提出一类随机动态过程基于一般q阶树的多尺度建模方法。首先,利用Markov过程的条件独立性给出一类过程基于q阶树的多尺度表示方法;其次,基于q阶树多尺度表示和具体实例推导出多尺度模型中的状态转移矩阵、扰动阵、初始状态和相应的协方差矩阵等的具体形式,为具有Markov统计特性的过程或信号建立起多尺度随机模型,这将为有效地解决多源同类信息和多源异类信息的数据融合等实际问题提供了理论基础;最后,给出一类Gauss-Markov过程基于三阶树和五阶树多尺度表示的计算机仿真结果,进一步验证建立模型的实用性和有效性。     

A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure

This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images in the original feature space without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudotraining set that is necessary for initializing an adequately defined binary semisupervised support vector machine classifier. Starting from these initial seeds, the performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. This algorithm models the full complexity of the change-detection problem, which is only partially represented from the seed pixels included in the pseudotraining set. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings. Experimental results obtained on different multispectral remote-sensing images confirm the effectiveness of the proposed approach.     

Modeling and analysis of stochastic differential equations driven by point processes

The modeling and analysis of nonlinear systems described by differential equations driven by point process noise are considered. The stochastic calculus of McShane is generalized to include such differential equations, and a more general canonical extension is defined. It is proved that this canonical extension possesses the same desirable properties for point process noise that it does for the noise processes, such as Brownian motion, considered by McShane. In addition, a new stochastic integral with respect to a point process is defined; this alternative integral obeys the rules of ordinary calculus. As a special case of the analysis of such systems, linear systems with multiplicative point process noise are investigated. The consistency of the canonical extension is studied by means of the product integral. Finally, moment equations and criteria for the stochastic stability of linear systems with multiplicative Poisson noise are derived.     

Design and Performance Analysis of a New Soft Handoff Scheme for CDMA Cellular Systems

In this paper, a new soft handoff scheme for CDMA cellular systems is proposed and investigated. It is pointed out that some handoff calls unnecessarily occupy multiple channels with little contribution to the performance of handoffs in IS95/CDMA2000-based handoff schemes or systems. To alleviate performance degradation due to channel resource shortage during soft handoff, a new concept of channel convertible set (CCS), which contains several types of handoff calls that unnecessarily occupy extra channels by considering the relative mobility of the calls in the handoff area is introduced. A new scheme that reallocates those extra channels in the CCS to new handoff calls when there is no available free channel in the system is proposed. Furthermore, according to the variation of the CCS, the proposed scheme dynamically adjusts the number of guard channels reserved exclusively for handoff. Then, the feasibility and implementation issues of the proposed scheme are discussed. To evaluate and compare performance indexes of different soft handoff schemes, continuous-time Markov chain models are constructed. Automated generation and solution of the underlying Markov chains are facilitated by stochastic reward net models, which are specified and solved by stochastic Petri net package. Numerical results show that this scheme can significantly decrease both the number of dropped handoff calls and the number of blocked calls without degrading the quality of communication service and the soft handoff process.     

The mean field theory in EM procedures for Markov random fields

In many signal processing and pattern recognition applications, the hidden data are modeled as Markov processes, and the main difficulty of using the maximisation (EM) algorithm for these applications is the calculation of the conditional expectations of the hidden Markov processes. It is shown how the mean field theory from statistical mechanics can be used to calculate the conditional expectations for these problems efficiently. The efficacy of the mean field theory approach is demonstrated on parameter estimation for one-dimensional mixture data and two-dimensional unsupervised stochastic model-based image segmentation. Experimental results indicate that in the 1-D case, the mean field theory approach provides results comparable to those obtained by Baum's (1987) algorithm, which is known to be optimal. In the 2-D case, where Baum's algorithm can no longer be used, the mean field theory provides good parameter estimates and image segmentation for both synthetic and real-world images     

A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps

A system for a regular updating of land-cover maps is proposed that is based on the use of multitemporal remote sensing images. Such a system is able to address the updating problem under the realistic but critical constraint that, for the image to be classified (i.e., the most recent of the considered multitemporal dataset) no ground truth information is available. The system is composed of an ensemble of partially unsupervised classifiers integrated in a multiple-classifier architecture. Each classifier of the ensemble exhibits the following novel characteristics: (1) it is developed in the framework of the cascade-classification approach to exploit the temporal correlation existing between images acquired at different times in the considered area; and (2) it is based on a partially unsupervised methodology capable of accomplishing the classification process under the aforementioned critical constraint. Both a parametric maximum-likelihood (ML) classification approach and a nonparametric radial basis function (RBF) neural-network classification approach are used as basic methods for the development of partially unsupervised cascade classifiers. In addition, in order to generate an effective ensemble of classification algorithms, hybrid ML and RBF neural-network cascade classifiers are defined by exploiting the characteristics of the cascade-classification methodology. The results yielded by the different classifiers are combined by using standard unsupervised combination strategies. This allows the definition of a robust and accurate partially unsupervised classification system capable of analyzing a wide typology of remote sensing data (e.g., images acquired by passive sensors, synthetic aperture radar images, and multisensor and multisource data). Experimental results obtained on a real multitemporal and multisource dataset confirm the effectiveness of the proposed system.