Integrating spatial statistics and remote sensing

This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine continuous spatial properties with classified map units. The paper is illustrated with five case studies: one on heavy metals in groundwater at different scales, one on soil variability within seemingly homogeneous units, one on fuzzy classification for a soillandscape model, one on classification with geostatistical procedures and one on thermal images. The integrated approach offers a better understanding and quantification of uncertainties in remote sensing studies.     

The enhancement of the cell-based GIS analyses with fuzzy processing capabilities

In order to store and process natural phenomena in Geographic Information Systems (GIS) it is necessary to model the real world to form computational representation. Since classical set theory is used in conventional GIS softwares to model uncertain real world, the natural variability in the environmental phenomena cannot be modeled appropriately. Because, pervasive imprecision of the real world is unavoidably reduced to artificially precise spatial entities when the conventional crisp logic is used for modeling.An alternative approach is the fuzzy set theory, which provides a formal framework to represent and reason with uncertain information. In addition, linguistic variable concept in a fuzzy logic system is useful for communicating concepts and knowledge with human beings. FuzzyCell is a system designed and implemented to enhance commercial GIS software, namely ArcMap^® with fuzzy set theory. FuzzyCell allows users to (a) incorporate human knowledge and experience in the form of linguistically defined variables into GIS-based spatial analyses, (b) handle imprecision in the decision-making processes, and (c) approximate complex ill-defined problems in decision-making processes and classification. It provides eight membership functions, inference methods, methods for rule aggregation, operators for set operations and methods for defuzzification.The operation of FuzzyCell is presented through case studies, which demonstrate its application for classification and decision-making processes. This paper shows how fuzzy logic approach may contribute to a better representation and reasoning with imprecise concepts, which are inherent characteristics of geographic data stored and processed in GIS.     

Evidential reasoning using extended fuzzy Dempster–Shafer theory for handling various facets of information deficiency

This work investigates the problem of combining deficient evidence for the purpose of quality assessment. The main focus of the work is modeling vagueness, ambiguity, and local nonspecificity in information within a unified approach. We introduce an extended fuzzy Dempster–Shafer scheme based on the simultaneous use of fuzzy interval‐grade and interval‐valued belief degree (IGIB). The latter facilitates modeling of uncertainties in terms of local ignorance associated with expert knowledge, whereas the former allows for handling the lack of information on belief degree assignments. Also, generalized fuzzy sets can be readily transformed into the proposed fuzzy IGIB structure. The reasoning for quality assessment is performed by solving nonlinear optimization problems on fuzzy Dempster–Shafer paradigm for the fuzzy IGIB structure. The application of the proposed inference method is investigated by designing a reasoning scheme for water quality monitoring and validated through the experimental data available for different sampling points in a water distribution network. © 2011 Wiley Periodicals, Inc.     

A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach

Knowledge inference systems are built to identify hidden and logical patterns in huge data. Decision trees play a vital role in knowledge discovery but crisp decision tree algorithms have a problem with sharp decision boundaries which may not be implicated to all knowledge inference systems. A fuzzy decision tree algorithm overcomes this drawback. Fuzzy decision trees are implemented through fuzzification of the decision boundaries without disturbing the attribute values. Data reduction also plays a crucial role in many classification problems. In this research article, it presents an approach using principal component analysis and modified Gini index based fuzzy SLIQ decision tree algorithm. The PCA is used for dimensionality reduction, and modified Gini index fuzzy SLIQ decision tree algorithm to construct decision rules. Finally, through PID data set, the method is validated in the simulation experiment in MATLAB.     

Application of self-organising maps for data mining with incomplete data sets

Self-organising maps (SOM) have become a commonly-used cluster analysis technique in data mining. However, SOM are not able to process incomplete data. To build more capability of data mining for SOM, this study proposes an SOM-based fuzzy map model for data mining with incomplete data sets. Using this model, incomplete data are translated into fuzzy data, and are used to generate fuzzy observations. These fuzzy observations, along with observations without missing values, are then used to train the SOM to generate fuzzy maps. Compared with the standard SOM approach, fuzzy maps generated by the proposed method can provide more information for knowledge discovery.     

Fuzzy Dempster–Shafer reasoning for rule-based classifiers

In real classification problems intrinsically vague information often coexist with conditions of “lack of specificity” originating from evidence not strong enough to induce knowledge, but only degrees of belief or credibility regarding class assignments. The problem has been addressed here by proposing a fuzzy Dempster–Shafer model (FDS) for multisource classification purposes. The salient aspect of the work is the definition of an empirical learning strategy for the automatic generation of fuzzy Dempster–Shafer classification rules from a set of exemplified training data. Dempster–Shafer measures of uncertainty are semantically related to conditions of ambiguity among the data and then automatically set during the learning process. Partial reduced beliefs in class assignments are then induced and explicitly represented when generating classification rules. The fuzzy deductive apparatus has been modified and extended to integrate the Dempster–Shafer propagation of evidence. The strategy has been applied to a standard classification problem in order to develop a sensitivity analysis in an easily controlled domain. A second experimental test has been conducted in the field of natural risk assessment, where vagueness and lack of specificity conditions are prevalent. These empirical tests show that classification benefits from the combination of the fuzzy and Dempster–Shafer models especially when conditions of lack of specifity among data are prevalent. ©1999 John Wiley & Sons, Inc.     

A robust design criterion for interpretable fuzzy models with uncertain data

We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness performance than the existing linear methods of estimation (H/sup 2/ and H/sup /spl infin// filtering techniques), because of the fact that not only linear parameters (consequents), but also the nonlinear parameters (membership functions) attempt to identify the uncertain behavior of the unknown system. However, the fuzzy identification methods must be robust to data uncertainties and modeling errors to ensure that the fuzzy approximation of unknown system's behavior is optimal in some sense. This study presents a deterministic approach to the robust design of fuzzy models in the presence of unknown but finite uncertainties in the identification data. We consider online identification of an interpretable fuzzy model, based on the robust solution of a regularized least-squares fuzzy parameters estimation problem. The aim is to resolve the difficulties associated with the robust fuzzy identification method due to lack of a priori knowledge about upper bounds on the data uncertainties. The study derives an optimal level of regularization that should be provided to ensure the robustness of fuzzy identification strategy by achieving an upper bound on the value of energy gain from data uncertainties and modeling errors to the estimation errors. A time-domain feedback analysis of the proposed identification approach is carried out with emphasis on stability, robustness, and steady-state issues. The simulation studies are provided to show the superiority of the proposed fuzzy estimation over the classical estimation methods.     

K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation

The K-means Iterative Fisher (KIF) algorithm is a robust, unsupervised clustering algorithm applied here to the problem of image texture segmentation. The KIF algorithm involves two steps. First, K-means is applied. Second, the K-means class assignments are used to estimate parameters required for a Fisher linear discriminant (FLD). The FLD is applied iteratively to improve the solution. This combined K-means and iterative FLD is referred to as the KIF algorithm. Two KIF implementations are presented: a mixture resolving approach is extended to an unsupervised binary hierarchical approach. The same binary hierarchical KIF algorithm is used to properly segment images even though the number of classes, the class spatial boundaries, and the number of samples per class vary. The binary hierarchical KIF algorithm is fully unsupervised, requires no a priori knowledge of the number of classes, is a non-parametric solution, and is computationally efficient compared to other methods used for clustering in image texture segmentation solutions. This unsupervised methodology is demonstrated to be an improvement over other published texture segmentation results using a wide variety of test imagery. Gabor filters and co-occurrence probabilities are used as texture features.     

Time sequence data mining using time–frequency analysis and soft computing techniques

This paper presents a new approach for time series data mining and knowledge discovery. The relevant features of non-stationary time series data from power network disturbances are extracted using a multiresolution S-transform which can be treated either as a phase corrected wavelet transform or a variable window short-time Fourier transform. After extracting the relevant features from the time series data, an integrated LVQ neural network and various feed-forward neural network architectures are used for pattern recognition of disturbance waveform data. The fuzzy MLP outperforms all the other different connectionist models and is used in the final stage for encoding knowledge in the connection weights that are used to generate rules for fuzzy inferencing of the disturbance patterns. Overall pattern classification accuracy of 99% is achieved for power signal time series data. The knowledge discovery from the data has then been presented for selected patterns using the new quantification procedures. The approach presented in this paper is a general one and can be applied to any time series data sequence for mining for similarities in the data.     

A min-max approach to fuzzy clustering, estimation, and identification

This study, for any unknown physical process y=f(x/sub 1/,...,x/sub n/), is concerned with the: 1) fuzzy partition of n-dimensional input space X=X/sub 1//spl times//spl middot//spl middot//spl middot//spl times/X/sub n/ into K different clusters, 2) estimating the process behavior y/spl circ/=f(x/spl circ/) for a given input x/spl circ/=(x/spl circ//sub 1/,/spl middot//spl middot//spl middot/,x/spl circ//sub n/)/spl isin/X, and 3) fuzzy approximation of the process, with uncertain input-output identification data {(x(k)/spl plusmn//spl delta/x/sub k/),(y(k)/spl plusmn/v/sub k/)}/sub k=1,.../, using a Sugeno type fuzzy inference system. A unified min-max approach (that attempts to minimize the worst-case effect of data uncertainties and modeling errors on estimation performance), is suggested to provide robustness against data uncertainties and modeling errors. The proposed method of min-max fuzzy parameters estimation does not make any assumption and does not require a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. To show the feasibility of the approach, simulation studies and a real-world application of physical fitness classification based on the fuzzy interpretation of physiological parameters, have been provided.     

Efficiency and accuracy of per-field classification for operational crop mapping

A crop map of The Netherlands was created using a methodology that integrates multi-temporal and multi-sensor satellite imagery, statistical data on crop area and parcel boundaries from a 1?:?10?000 digital topographic map. In the first phase a crop field database was created by extracting static parcel boundaries from the digital topographic map and by adding dynamic crop boundaries using on-screen digitizing. In the next phase the crop type was determined from the spectral and phenological properties of each field. The resulting crop map has an accuracy larger than 80% for most individual crops and an overall accuracy of 90%. By comparing cost and man-hours it was demonstrated that per-field classification is more efficient than per-pixel classification and decreased the effort for classification from 1500 to 500 man-hours, but the effort for creating the crop field database was estimated at 2300 man-hours. The use of image segmentation techniques for deriving the crop field database was discussed. It was concluded that image segmentation cannot replace the use of a large-scale topographic map but, in the future, image segmentation may be used to map the dynamic crop boundaries within the topographic parcels.     

中文文本的关键词自动抽取和模糊分类

本文提出了中文文本分类的两种模糊方法,一种基于模糊集间的语义距离,一种基于本文中提出的‘模糊分类网络’。两者都必须首先从文本中抽取关键词集合,本文给出了一种主要采用统计方法结合受限自然语言理解技术的模糊关键词集合提取方法,它与模糊分类方法结合,可望达到文本信息的自动分类。所提出的方法同样适合于模式识别之类问题的解决。     

A GAs based approach for mining breast cancer pattern

Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this paper, a genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy. Early many studies of handling the breast cancer diagnostic problems used the statistical related techniques. As the diagnosis of breast cancer is highly nonlinear in nature, it is hard to develop a comprehensive model taking into account all the independent variables using conventional statistical approaches. Recently, numerous studies have demonstrated that neural networks (NNs) are more reliable than the traditional statistical approaches and the dynamic stress method. The usefulness of using NNs have been reported in literatures but the most obstacle is the in the building and using the model in which the classification rules are hard to be realized. We compared our results against a commercial data mining software, and we show experimentally that the proposed rule extraction approach is promising for improving prediction accuracy and enhancing the modeling simplicity. In particular, our approach is capable of extracting rules which can be developed as a computer model for prediction or classification of breast cancer potential like expert systems.     

Knowledge discovery in time series databases

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series: stock-market data and weather data.     

Non-singleton genetic fuzzy logic system for arrhythmias classification

This paper aims at analyzing a non-singleton fuzzy logic classifier (NSFLC) and assessing its ability to cope with uncertainties in pattern classification problems. The analysis demonstrate that the NSFLC has fuzzy classification boundary and noise suppression capability. These characteristics means that the NSFLC is particulary suitable for problems where the boundaries between classes is non-distinct. To further demonstrate the benefits offered by a NSFLC, a non-singleton fuzzy logic classifier evolved using Genetic Algorithm (GA) is assessed using a benchmark cardiac arrhythmias classification problem. Results indicate that a NSFLC achieved good classification accuracy using features that are easier to extract, but contain more uncertainties.     

A hybrid coevolutionary algorithm for designing fuzzy classifiers

Rule learning is one of the most common tasks in knowledge discovery. In this paper, we investigate the induction of fuzzy classification rules for data mining purposes, and propose a hybrid genetic algorithm for learning approximate fuzzy rules. A novel niching method is employed to promote coevolution within the population, which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run. In order to improve the quality of the learned rules, a local search method was devised to perform fine-tuning on the offspring generated by genetic operators in each generation. After the GA terminates, a fuzzy classifier is built by extracting a rule set from the final population. The proposed algorithm was tested on datasets from the UCI repository, and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods.     

A Modified Double Gyre with Ground Truth Hyperbolic Trajectories for Flow Visualization

The model of a Double Gyre flow by Shadden et al. is a standard benchmark data set for the computation of hyperbolic Lagrangian Coherent Structures (LCS) in flow data. While structurally extremely simple, it generates hyperbolic LCS of arbitrary complexity. Unfortunately, the Double Gyre does not come with a well‐defined ground truth: the location of hyperbolic LCS boundaries can only be approximated by numerical methods that usually involve the gradient of the flow map. We present a new benchmark data set that is a small but carefully designed modification of the Double Gyre , which comes with ground truth closed‐form hyperbolic trajectories. This allows for computing hyperbolic LCS boundaries by a simple particle integration without the consideration of the flow map gradient. We use these hyperbolic LCS as a ground truth solution for testing an existing numerical approach for extracting hyperbolic trajectories. In addition, we are able to construct hyperbolic LCS curves that are significantly longer than in existing numerical methods.     

Fuzzy expert system with double knowledge base for ultrasonic classification

This article shows a pattern recognition method for object classification using ultrasonic sensors and a dual knowledge base fuzzy expert system. The developed system uses a pair of ultrasonic sensors for obtaining information about the object shape from the ultrasonic echo signal envelope. In order to reduce the size of the database, a set of parameters is calculated for extracting knowledge about the object. However, the information provided by ultrasonic sensors contains a very high uncertainty level. This uncertainty is caused by several environmental effects, which are very difficult to eliminate in industrial applications. Among these environment factors are the air temperature and humidity, the air movement, etc. They create variations in the proprieties of the medium and disturbances during the acoustic propagation process. The presented system has been specially designed for industrial applications, where it is very difficult to reduce these disturbances and where it is necessary to use intelligent systems with high autonomy. The fuzzy expert system proposed has a dual knowledge base, that is, a statistical knowledge located on the memberships functions, and the standard rule-based knowledge. This expert system deals with the uncertainties in the information, and it is able to generate and modify the knowledge base and the decision rules in an automatic way. Furthermore, it is able to adapt the knowledge base to the slow changes produced by disturbing factors, such as humidity and temperature. On the other hand, because this system maintains a rule-based structure it is very easy to incorporate expert human knowledge.     

Deterministic approach to robust adaptive learning of fuzzy models

This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of H/sup /spl infin// estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.     

Spectral-spatial classification for hyperspectral imagery: a novel combination method based on affinity scoring高光谱图像的空谱分类: 基于隶属度评分的新的合并方法

Recently, a general framework for spectral-spatial classification has caught the attention of the hyperspectral imagery (HSI) society. It consists of three parts: classification, segmentation and combination of the former results to make a refined labeled map. Seeing the potentials of the last part, we derive a novel combination rule based on affinity scoring (CRAS). The core of the system is affinity score (AS), which is derived from fuzzy logic. Every AS measures the degree, i.e., the affinity, by which a pixel belongs to a class. The score is essentially decided by three factors: local spatial consistency, spectral similarity, and prior knowledge. The method is compatible with basic classification and segmentation tools, thus saving the trouble of designing complex techniques for the other parts in the framework. Experimental results show that CRAS excels several basic techniques as well as various state-of-the-art methods in the area of spectral-spatial classification.