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Human intervention and natural phenomenon cause change in land use day by day. Availability of accurate land use information is essential for many applications like natural resource management, planning and monitoring programs. Landuse Change has become a central component in current strategies for managing natural resources and monitoring environmental change. Because of the rapid development in the field of land use mapping, there is an increase in studies of land use change worldwide. Providing an accurate assessment of the extent and health of the world’s forest, grassland and agricultural resources has become an important priority. By printed maps without any statistics or only statistics without any map can not solve this visualization problem. Because printed maps have not attracted as much attention as statistics among the people because of it is limited applications (Himiyama, 2002). Remotely sensed data like aerial photographs and satellite imageries are undoubtedly the most ideal data for extracting land use change information. Satellite images are the most economical way of getting data for different times. The multitude of existing software helps getting information from satellite image also in manipulating the information. The approach used in this study to classify satellite images and change detection based on Satellite images Landsat MSS (1972), Landsat TM (1989) and Landsat ETM (1999) for using supervised classification methods like maximum likelihood (MAXLIKE), MAHALCLASS and time series analysis of CROSSTAB. After performed these hard and soft classifiers the research showed the significant Landuse change in the study area of Chakoria Sundarban mangrove forest. Remote sensing is the modern tools for detecting change pattern and behaviours of coastal environment (Saifuzzaman, 2000). So, those tools are used in the research work for better change analysis of the study area. For analyzing, evaluation and mapping environmental change detection of different years remotely sensed data have been undertaken. The present research provides some suggestions and recommendations as per research findings in order to optimize the utility of coastal resources and to maintain the sustainability of the resources, coastal land use control and there by stabilizing the coastal vulnerable area of chakoria Sundarban.

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised classification tasks. In this thesis, we propose new algorithms for solving semi-supervised binary classification problem using GP regression (GPR) models. The algorithms are closely related to semi-supervised classification based on support vector regression (SVR) and maximum margin clustering. The proposed algorithms are simple and easy to implement. Also, the hyper-parameters are estimated without resorting to expensive cross-validation technique. The algorithm based on sparse GPR model gives a sparse solution directly unlike the SVR based algorithm. Use of sparse GPR model helps in making the proposed algorithm scalable. The results of experiments on synthetic and real-world datasets demonstrate the efficacy of proposed sparse GP based algorithm for semi-supervised classification.

<p>Human intervention and natural phenomenon cause change in land use day by day. Availability of accurate land use information is essential for many applications like natural resource management, planning and monitoring programs. Landuse Change has become a central component in current strategies for managing natural resources and monitoring environmental change. Because of the rapid development in the field of land use mapping, there is an increase in studies of land use change worldwide. Providing an accurate assessment of the extent and health of the world’s forest, grassland and agricultural resources has become an important priority. By printed maps without any statistics or only statistics without any map can not solve this visualization problem. Because printed maps have not attracted as much attention as statistics among the people because of it is limited applications (Himiyama, 2002). Remotely sensed data like aerial photographs and satellite imageries are undoubtedly the most ideal data for extracting land use change information. Satellite images are the most economical way of getting data for different times. The multitude of existing software helps getting information from satellite image also in manipulating the information. The approach used in this study to classify satellite images and change detection based on Satellite images Landsat MSS (1972), Landsat TM (1989) and Landsat ETM (1999) for using supervised classification methods like maximum likelihood (MAXLIKE), MAHALCLASS and time series analysis of CROSSTAB. After performed these hard and soft classifiers the research showed the significant Landuse change in the study area of Chakoria Sundarban mangrove forest. Remote sensing is the modern tools for detecting change pattern and behaviours of coastal environment (Saifuzzaman, 2000). So, those tools are used in the research work for better change analysis of the study area. For analyzing, evaluation and mapping environmental change detection of different years remotely sensed data have been undertaken. The present research provides some suggestions and recommendations as per research findings in order to optimize the utility of coastal resources and to maintain the sustainability of the resources, coastal land use control and there by stabilizing the coastal vulnerable area of chakoria Sundarban.</p><p> </p>

Vaizdų analizė šiomis dienomis yra labai svarbi dėl plataus pritaikymo daugelyje mokslo ir pramonės sričių. Vienas iš vaizdų analizės įrankių – objekto atpažinimas (klasifikavimas) (angl. pattern recognition). Statistinis objekto atpažinimas, paremtas Bajeso diskriminantinėmis funkcijomis – šio darbo objektas. Sprendžiama problema – optimalus klasifikavimas stacionaraus Gauso atsitiktinio lauko (GRF) stebinio, į vieną iš dviejų klasių, laikant, kad jis yra priklausomas nuo mokymo imties ir atsižvelgiant į jo ryšius su mokymo imtimi. Pateikta klasifikavimo procedūra, kuri Gauso atsitiktinio lauko stebinius klasifikuoja optimaliai. Yra pasiūlytas naujas klasifikavimo su mokymu metodas, kuris duoda geresnius rezultatus, lyginant su įprastai naudojamomis Bajeso diskriminantinėmis funkcijomis. Metodas realizuotas R sistemos aplinkoje ir tikrinamas eksperimentų būdu, atstatant vaizdus, sugadintus erdvėje koreliuoto triukšmo. Tokia situacija pasitaiko natūraliai, pavyzdžiui, degant miškui dūmai uždengia nuotolinio stebėjimo vaizdą, gautą iš palydovo. Taip pat tokia situacija gana dažna esant debesuotumui. Esant tokiai situacijai erdvinės priklausomybės įvedimas į klasifikacijos problemą pasiteisina. Pateiktos (išvestos) analitinės klaidų tikimybių išraiškos Bajeso diskriminantinėms funkcijoms, kurios yra kaip šių funkcijų veikimo kriterijus. Ištirta klaidų tikimybių priklausomybė nuo statistinių parametrų reikšmių. / Image analysis is very important because of its usage in many different areas of science and industry. Pattern recognition (classification) is a tool used in image analysis. Statistical pattern recognition, based on Bayes discriminant functions is the object of this work. The main problem is to classify stationary Gaussian random field observation into one off two classes, considering, that it is dependant on training sample ant taking in to account the relationship with training sample. The new supervised classification method, based on Bayes discriminant functions, is proposed and it gives better results comparing with other commonly used Bayes discriminant functions. Method is programmed with R program and investigated experimentally, reconstructing images corrupted by spatially correlated noise. Such situation occurs naturally, for example, during the forest fire smoke covers the remotely sensed image, gathered from the satellite. Also such situation is often during cloudy days. During such situation the incorporation of the spatial dependences into the classification problem is useful. Analytical error rates of Bayes discriminant functions are presented (derived), which are the criterion of these functions. Also, the dependences on statistical parameters are investigated for these error rates.

Les documents publiés par des entreprises, tels les communiqués de presse, contiennent une foule d’informations sur diverses activités des entreprises. C’est une source précieuse pour des analyses en intelligence d’affaire. Cependant, il est nécessaire de développer des outils pour permettre d’exploiter cette source automatiquement, étant donné son grand volume. Ce mémoire décrit un travail qui s’inscrit dans un volet d’intelligence d’affaire, à savoir la détection de relations d’affaire entre les entreprises décrites dans des communiqués de presse. Dans ce mémoire, nous proposons une approche basée sur la classification. Les méthodes de classifications existantes ne nous permettent pas d’obtenir une performance satisfaisante. Ceci est notamment dû à deux problèmes : la représentation du texte par tous les mots, qui n’aide pas nécessairement à spécifier une relation d’affaire, et le déséquilibre entre les classes. Pour traiter le premier problème, nous proposons une approche de représentation basée sur des mots pivots c’est-à-dire les noms d’entreprises concernées, afin de mieux cerner des mots susceptibles de les décrire. Pour le deuxième problème, nous proposons une classification à deux étapes. Cette méthode s’avère plus appropriée que les méthodes traditionnelles de ré-échantillonnage. Nous avons testé nos approches sur une collection de communiqués de presse dans le domaine automobile. Nos expérimentations montrent que les approches proposées peuvent améliorer la performance de classification. Notamment, la représentation du document basée sur les mots pivots nous permet de mieux centrer sur les mots utiles pour la détection de relations d’affaire. La classification en deux étapes apporte une solution efficace au problème de déséquilibre entre les classes. Ce travail montre que la détection automatique des relations d’affaire est une tâche faisable. Le résultat de cette détection pourrait être utilisé dans une analyse d’intelligence d’affaire. / Documents published by companies such as press releases, contain a wealth of information on various business activities. This is a valuable source for business intelligence analysis; but automatic tools are needed to exploit such large volume data. The work described in this thesis is part of a research project on business intelligence, namely we aim at the detection of business relationships between companies described in press releases. In this thesis, we consider business relation detection as a problem of classification. However, the existing classification methods do not allow us to obtain a satisfactory performance. This is mainly due to two problems: the representation of text using all the content words, which do not necessarily a business relationship; and the imbalance between classes. To address the first problem, we propose representations based on words that are between or close to the names of companies involved (which we call pivot words) in order to focus on words having a higher chance to describe a relation. For the second problem, we propose a two-stage classification. This method is more effective than the traditional resampling methods. We tested our approach on a collection of press releases in the automotive industry. Our experiments show that both proposed approaches can improve the classification performance. They perform much better than the traditional feature selection methods and the resampling method. This work shows the feasibility of automatic detection of business relations. The result of this detection could be used in an analysis of business intelligence.

Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Supervised classifiers are commonly used in remote sensing to extract land cover information. They are, however, limited in their ability to cost-effectively produce sufficiently accurate land cover maps. Various factors affect the accuracy of supervised classifiers. Notably, the number of available training samples is known to significantly influence classifier performance and to obtain a sufficient number of samples is not always practical. The support vector machine (SVM) does perform well with a limited number of training samples. But little research has been done to evaluate SVM’s performance for geographical object-based image analysis (GEOBIA). GEOBIA also allows the easy integration of additional features into the classification process, a factor which may significantly influence classification accuracies. As such, two experiments were developed and implemented in this research. The first compared the performances of object-based SVM, maximum likelihood (ML) and nearest neighbour (NN) classifiers using varying training set sizes. The effect of feature dimensionality on classifier accuracy was investigated in the second experiment. A SPOT 5 subscene and a four-class classification scheme were used. For the first experiment, training set sizes ranging from 4-20 per land cover class were tested. The performance of all the classifiers improved significantly as the training set size was increased. The ML classifier performed poorly when few (<10 per class) training samples were used and the NN classifier performed poorly compared to SVM throughout the experiment. SVM was the superior classifier for all training set sizes although ML achieved competitive results for sets of 12 or more training samples per class. Training sets were kept constant (20 and 10 samples per class) for the second experiment while an increasing number of features (1 to 22) were included. SVM consistently produced superior classification results. SVM and NN were not significantly (negatively) affected by an increase in feature dimensionality, but ML’s ability to perform under conditions of large feature dimensionalities and few training areas was limited. Further investigations using a variety of imagery types, classification schemes and additional features; finding optimal combinations of training set size and number of features; and determining the effect of specific features should prove valuable in developing more costeffective ways to process large volumes of satellite imagery. KEYWORDS Supervised classification, land cover, support vector machine, nearest neighbour classification maximum likelihood classification, geographic object-based image analysis / AFRIKAANSE OPSOMMING: Gerigte klassifiseerders word gereeld aangewend in afstandswaarneming om inligting oor landdekking te onttrek. Sulke klassifiseerders het egter beperkte vermoëns om akkurate landdekkingskaarte koste-effektief te produseer. Verskeie faktore het ʼn uitwerking op die akkuraatheid van gerigte klassifiseerders. Dit is veral bekend dat die getal beskikbare opleidingseenhede ʼn beduidende invloed op klassifiseerderakkuraatheid het en dit is nie altyd prakties om voldoende getalle te bekom nie. Die steunvektormasjien (SVM) werk goed met beperkte getalle opleidingseenhede. Min navorsing is egter gedoen om SVM se verrigting vir geografiese objek-gebaseerde beeldanalise (GEOBIA) te evalueer. GEOBIA vergemaklik die integrasie van addisionele kenmerke in die klassifikasie proses, ʼn faktor wat klassifikasie akkuraathede aansienlik kan beïnvloed. Twee eksperimente is gevolglik ontwikkel en geïmplementeer in hierdie navorsing. Die eerste eksperiment het objekgebaseerde SVM, maksimum waarskynlikheids- (ML) en naaste naburige (NN) klassifiseerders se verrigtings met verskillende groottes van opleidingstelle vergelyk. Die effek van kenmerkdimensionaliteit is in die tweede eksperiment ondersoek. ʼn SPOT 5 subbeeld en ʼn vier-klas klassifikasieskema is aangewend. Opleidingstelgroottes van 4-20 per landdekkingsklas is in die eerste eksperiment getoets. Die verrigting van die klassifiseerders het beduidend met ʼn toename in die grootte van die opleidingstelle verbeter. ML het swak presteer wanneer min (<10 per klas) opleidingseenhede gebruik is en NN het, in vergelyking met SVM, deurgaans swak presteer. SVM het die beste presteer vir alle groottes van opleidingstelle alhoewel ML kompeterend was vir stelle van 12 of meer opleidingseenhede per klas. Die grootte van die opleidingstelle is konstant gehou (20 en 10 eenhede per klas) in die tweede eksperiment waarin ʼn toenemende getal kenmerke (1 tot 22) toegevoeg is. SVM het deurgaans beter klassifikasieresultate gelewer. SVM en NN was nie beduidend (negatief) beïnvloed deur ʼn toename in kenmerkdimensionaliteit nie, maar ML se vermoë om te presteer onder toestande van groot kenmerkdimensionaliteite en min opleidingsareas was beperk. Verdere ondersoeke met ʼn verskeidenheid beelde, klassifikasie skemas en addisionele kenmerke; die vind van optimale kombinasies van opleidingstelgrootte en getal kenmerke; en die bepaling van die effek van spesifieke kenmerke sal waardevol wees in die ontwikkelling van meer koste effektiewe metodes om groot volumes satellietbeelde te prosesseer. TREFWOORDE Gerigte klassifikasie, landdekking, steunvektormasjien, naaste naburige klassifikasie, maksimum waarskynlikheidsklassifikasie, geografiese objekgebaseerde beeldanalise

Ce travail de thèse s’inscrit dans le cadre d’une Convention Industrielle de Formation par la REcherche (CIFRE) ayant pour objectif la mise en place de solutions innovantes pour le diagnostic de défaillances. Il s’agit de répondre au besoin de la société Zodiac Actuation Systems afin de diagnostiquer les défaillances pouvant survenir sur leurs systèmes d’actionnement de sièges d’avion. Premièrement, le cadre ainsi que les motivations de l’étude sont exposés. Ensuite un état de l’art sur les méthodes de diagnostic de défaillances est donné. Puis la problématique de l’hybridation de ces méthodes est abordée. Ceci a permis d’adopter la méthode de classification supervisée pour le diagnostic. Ensuite, les campagnes de mesures, le processus de construction des bases de données ainsi que les différents algorithmes nécessaires pour la classification sont présentés. Une expérimentation sur la partie du dossier d’un siège d’avion est exposée et les résultats sont donnés. Afin d’améliorer les résultats obtenus, une approche de classification renforcée par des observateurs d’état est proposée et appliquée sur le dossier du siège. Ce renforcement est réalisé à l’aide des données estimées par les observateurs tout en construisant des bases de données augmentées. Trois types d’observateurs, linéaire, Takagi-Sugeno (TS) et TS à entrées inconnues (TSEI) sont employés. L’observateur TSEI apparait comme le mieux adapté à notre application. Finalement, une extension de l'approche proposée sur l’ensemble du siège d’avion est proposée. Celle-ci consiste en la mise en œuvre d’observateurs décentralisés TSEI pour chaque sous-ensemble du siège en tenant compte de leurs interconnexions. Ces derniers ont permis d’améliorer les résultats de détection de défaillances sur l’ensemble du siège d’avion. / This study was supported by Zodiac Actuation Systems within the framework of a ``CIFRE'' project which aims to design a Fault Detection and Diagnosis (FDD) approach for actuation systems of passengers seats in commercial aircrafts. First of all, the industrial context as well as the motivations of our project have been explained. Then, a state of the art on FDD methods is presented. Among them, hybridization of FDD methods can be found and seems interesting to our application. In a first step, the supervised classification method for the FDD has been considered. To do this, the process measurements and the concept of databases construction are presented. Then, different types of classification algorithms are explained. From experimental measurements, the classification results for FDD purpose on the recline of the seat are given. In a second step, an enhanced classification approach is proposed. It consists in estimating non-measurable variables by the state observers. These variables are then added, as estimated attributes, to the measured database. The aim is to enrich the knowledge used by the classifier and thus to improve the rate of FDD. Three types of state observers are considered: linear, then Takagi-Sugeno (TS) and Unknown Input Takagi-Sugeno (UITS) observers. It appears that the UITS observer-based results are more accurate for our application. Finally, the proposed FDD approach is extended to the hole of the seat by considering a decentralized approach. In this context, decentralized UITS are proposed for each segment of the seat by taking into account their interconnexions. It is shown that these decentralized observers improve the FDD results of the considered aircraft seat.

En imagerie hyperspectrale, chaque pixel est associé à un spectre provenant de la réflectance observée en d points de mesure (i.e., longueurs d'onde). On se retrouve souvent dans une situation où la taille d'échantillon n est relativement faible devant le nombre d de variables. Ce phénomène appelé "fléau de la dimension" est bien connu en statistique multivariée. Plus d augmente devant n, plus les performances des méthodologies statistiques standard se dégradent. Les spectres de réflectance intègrent dans leur dimension spectrale un continuum qui leur confère une nature fonctionnelle. Un hyperspectre peut être modélisé par une fonction univariée de la longueur d'onde, sa représentation produisant une courbe. L'utilisation de méthodes fonctionnelles sur de telles données permet de prendre en compte des aspects fonctionnels tels que la continuité, l'ordre des bandes spectrales, et de s'affranchir des fortes corrélations liées à la finesse de la grille de discrétisation. L'objectif principal de cette thèse est d'évaluer la pertinence de l'approche fonctionnelle dans le domaine de la télédétection hyperspectrale lors de l'analyse statistique. Nous nous sommes focalisés sur le modèle non-paramétrique de régression fonctionnelle, couvrant la classification supervisée. Dans un premier temps, l'approche fonctionnelle a été comparée avec des méthodes multivariées usuellement employées en télédétection. L'approche fonctionnelle surpasse les méthodes multivariées dans des situations délicates où l'on dispose d'une petite taille d'échantillon d'apprentissage combinée à des classes relativement homogènes (c'est-à-dire difficiles à discriminer). Dans un second temps, une alternative à l'approche fonctionnelle pour s'affranchir du fléau de la dimension a été développée à l'aide d'un modèle parcimonieux. Ce dernier permet, à travers la sélection d'un petit nombre de points de mesure, de réduire la dimensionnalité du problème tout en augmentant l'interprétabilité des résultats. Dans un troisième temps, nous nous sommes intéressés à la situation pratique quasi-systématique où l'on dispose de données fonctionnelles contaminées. Nous avons démontré que pour une taille d'échantillon fixée, plus la discrétisation est fine, meilleure sera la prédiction. Autrement dit, plus d est grand devant n, plus la méthode statistique fonctionnelle développée est performante. / In hyperspectral imaging, each pixel is associated with a spectrum derived from observed reflectance in d measurement points (i.e., wavelengths). We are often facing a situation where the sample size n is relatively low compared to the number d of variables. This phenomenon called "curse of dimensionality" is well known in multivariate statistics. The mored increases with respect to n, the more standard statistical methodologies performances are degraded. Reflectance spectra incorporate in their spectral dimension a continuum that gives them a functional nature. A hyperspectrum can be modelised by an univariate function of wavelength and his representation produces a curve. The use of functional methods allows to take into account functional aspects such as continuity, spectral bands order, and to overcome strong correlations coming from the discretization grid fineness. The main aim of this thesis is to assess the relevance of the functional approach in the field of hyperspectral remote sensing for statistical analysis. We focused on the nonparametric fonctional regression model, including supervised classification. Firstly, the functional approach has been compared with multivariate methods usually involved in remote sensing. The functional approach outperforms multivariate methods in critical situations where one has a small training sample size combined with relatively homogeneous classes (that is to say, hard to discriminate). Secondly, an alternative to the functional approach to overcome the curse of dimensionality has been proposed using parsimonious models. This latter allows, through the selection of few measurement points, to reduce problem dimensionality while increasing results interpretability. Finally, we were interested in the almost systematic situation where one has contaminated functional data. We proved that for a fixed sample size, the finer the discretization, the better the prediction. In other words, the larger dis compared to n, the more effective the functional statistical methodis.

Orientador: Prof. Dr. Alexandre Noma / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2016. / A classificação supervisionada consiste em utilizar uma base de dados rotulada para avaliar o desempenho de um determinado classifcador. Mensurando tal desempenho, podemos inferir se, para o problema abordado, tal classifcador poderá ser empregado ou não. Métodos classicos de classificação utilizam um unico classifcador para a analise de um problema. Uma forma de melhorar o desempenho da classificação é empregar técnicas que misturam classifcadores, sejam com base em seus resultados ou nas caracteristicas intrinsecas que cada classicador possui. Neste trabalho, foram empregados os métodos Votação e Adaboost para combinar classifcadores e utilizando base de dados rotuladas provenientes de imagens satelitais extraídas da regi~ao da Amazonia Legal para classificar a cobertura do solo. Resultados obtidos mostraram que o algoritmo SVM por si so consegue resultados de classificação em torno dos 90% em casos gerais. Para casos especifios, a empregabilidade do Adaboost resultou em um acrescimo de, aproximadamente, 10% na taxa de acurácia para um tipo de classe em comparação o com o melhor resultado dos métodos tradicionais. / Supervised classification is based on using a labeled database to evaluate a given classifer's performance. Measuring such performance, it is possible to infer if, for the problem addressed, such a classifer can be employed or not. Classical classification methods use a single classier to analyze a problem. One way to improve classifcation's performance is to employ techniques that mix classifers, based on their results or by each classifer's intrinsic characteristics. In this paper, the methods Voting and Adaboost were used to combine classifers and using labeled data bases from satellite's images extracted from the Legal Amazon region to classify the soil cover. Results obtained showed that the SVM algorithm alone achieves classifcation results around 90 % in general cases. For specific cases, the employability of Adaboost resulted in an increase of approximately 10 % in the accuracy rate for a class type compared to the best result of the traditional methods.