Method for solving nonlinearity in recognising tropical wood species

Classifying tropical wood species pose a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. Hence, an automatic tropical wood species recognition system was developed at Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia. The system classifies wood species based on texture analysis whereby wood surface images are captured and wood features are extracted from these images which will be used for classification. Previous research on tropical wood species recognition systems considered methods for wood species classification based on linear features. Since wood species are known to exhibit nonlinear features, a Kernel-Genetic Algorithm (Kernel-GA) is proposed in this thesis to perform nonlinear feature selection. This method combines the Kernel Discriminant Analysis (KDA) technique with Genetic Algorithm (GA) to generate nonlinear wood features and also reduce dimension of the wood database. The proposed system achieved classification accuracy of 98.69%, showing marked improvement to the work done previously. Besides, a fuzzy logic-based pre-classifier is also proposed in this thesis to mimic human interpretation on wood pores which have been proven to aid the data acquisition bottleneck and serve as a clustering mechanism for large database simplifying the classification. The fuzzy logic-based pre-classifier managed to reduce the processing time for training and testing by more than 75% and 26% respectively. Finally, the fuzzy pre-classifier is combined with the Kernal-GA algorithm to improve the performance of the tropical wood species recognition system. The experimental results show that the combination of fuzzy preclassifier and nonlinear feature selection improves the performance of the tropical wood species recognition system in terms of memory space, processing time and classification accuracy

Multitemporální klasifikace zemědělských plodin pomocí dat Sentinel-1

Multi-temporal classification of agricultural crops using Sentinel-1 Abstract This diploma thesis aimed on the exploration of the reflective behavior of individual agricultural crops during the vegetation season. Statistical analysis of agricultural crops was carried out on the basis of multi-temporal SAR C-band Sentinel-1 data. The crop's backscatter was observed during the year 2016. Classification rules were made from detected characteristics. Achieved knowledge was applied and crops separation was done. The result of separation was successful in class Maize. Spring and Winter grains was impossible to distinguish. The possible reasons of poor results are mentioned and further improvements are suggested. Keywords: SAR, C-Band, crops, object-based classification, SENTINEL-1Multitemporální klasifikace zemědělských plodin pomocí dat Sentinel-1 Abstrakt Diplomová práce je zaměřena na zkoumání odrazivých vlastností jednotlivých zemědělských plodin. Statistické veličiny byly zkoumány s využitím multitemporálních radarových dat Sentinel-1 pásma C, poskytovaných Evropskou kosmickou agenturou. Odrazivost vybraných plodin byla pozorována v průběhu roku 2016. Ze zjištěných projevů odrazivosti byla vytvořena klasifikační pravidla. Pomocí zjištěných pravidel byla provedena klasifikace jednotlivých plodin. Úspěšná odlišitelnost byla prokázána pouze u třídy kukuřice. Jarní a ozimé obiloviny nebylo možné spolehlivě rozlišit. Jsou zmíněny možné důvody nepřesností a zároveň navrženy úpravy a metody pro budoucí zlepšení. Klíčová slova: SAR, C-Band, zemědělské plodiny, objektová klasifikace, Senitnel-1Katedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyPřírodovědecká fakultaFaculty of Scienc

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Untersuchungen zum Einsatz von Flugzeug-InSAR in der Gebirgskartographie

The aim of this thesis is to determine to what extent aircraft-borne radar remote sensing can be used as the sole method for making recordings of the surface of the earth as a basis for compiling topographical and relief maps of mountainous areas. This is done using three test areas: the Edelsberg area in the Allgäu Alps and the Silvretta and Verwall Groups in the Central Alps. The basis for discussion is provided by examination of the interaction between the objects to be imaged and the radar signal, the sensor-specific characteristics thereby being taken into account. Following this some data processing and conditioning methods used for extracting information on the relief and surface coverage for preparation of cartographical products are presented. Analysis of the quality of the results shows that, measured against the requirements of mountain cartography, radar remote sensing is a practical and useful tool for making maps in Alpine regions. As the sole source of information, however, aircraft-borne radar remote sensing p roves to date to be inadequate for cartographical applications in high-mountain regions.Ziel der vorliegenden Arbeit ist, festzustellen, inwieweit die flugzeuggetragene Radarfernerkundung als alleinige Erfassungsmethode der Erdoberfläche zur Erstellung von topographischen und reliefbeschreibenden Karten in Gebirgslandschaften dienen kann. Dies wird anhand von drei Testgebieten, dem Edelsberggebiet in den Allgäuer Alpen sowie der Silvretta- und Verwallgrupe in den Zentralalpen, untersucht. Die Betrachtung der Interaktion zwischen den abzubildenden Objekten und dem Radar-Signal unter Berücksichtigung der sensorspezifischen Charakteristika bildet dabei die Diskussionsgrundlage. Im weiteren werden Methoden zur Datenprozessierung und -aufbereitung vorgestellt, die eine Informa-tionsextraktion bezüglich des Reliefs und der Oberflächenbedeckung für die Erstellung kartographischer Produkte ermöglichen. Die Qualitätsanalyse der Ergebnisse zeigt, dass die Radarfernerkundung, gemessen an den Anforderungen der Gebirgskartographie, ein sinnvolles und nutzbringendes Werkzeug für die Kartenerstellung in alpinen Regionen ist. Für kartographische Anwendungen in Hochgebirgsregionen erweist sich die flugzeuggetragene Radarfernerkundung als einzige Informationsquelle bislang allerdings als nicht ausreichend

Modélisation stochastique pour l'analyse d'images texturées (approches Bayésiennes pour la caractérisation dans le domaine des transformées)

Le travail présenté dans cette thèse s inscrit dans le cadre de la modélisation d images texturées à l aide des représentations multi-échelles et multi-orientations. Partant des résultats d études en neurosciences assimilant le mécanisme de la perception humaine à un schéma sélectif spatio-fréquentiel, nous proposons de caractériser les images texturées par des modèles probabilistes associés aux coefficients des sous-bandes. Nos contributions dans ce contexte concernent dans un premier temps la proposition de différents modèles probabilistes permettant de prendre en compte le caractère leptokurtique ainsi que l éventuelle asymétrie des distributions marginales associées à un contenu texturée. Premièrement, afin de modéliser analytiquement les statistiques marginales des sous-bandes, nous introduisons le modèle Gaussien généralisé asymétrique. Deuxièmement, nous proposons deux familles de modèles multivariés afin de prendre en compte les dépendances entre coefficients des sous-bandes. La première famille regroupe les processus à invariance sphérique pour laquelle nous montrons qu il est pertinent d associer une distribution caractéristique de type Weibull. Concernant la seconde famille, il s agit des lois multivariées à copules. Après détermination de la copule caractérisant la structure de la dépendance adaptée à la texture, nous proposons une extension multivariée de la distribution Gaussienne généralisée asymétrique à l aide de la copule Gaussienne. L ensemble des modèles proposés est comparé quantitativement en terme de qualité d ajustement à l aide de tests statistiques d adéquation dans un cadre univarié et multivarié. Enfin, une dernière partie de notre étude concerne la validation expérimentale des performances de nos modèles à travers une application de recherche d images par le contenu textural. Pour ce faire, nous dérivons des expressions analytiques de métriques probabilistes mesurant la similarité entre les modèles introduits, ce qui constitue selon nous une troisième contribution de ce travail. Finalement, une étude comparative est menée visant à confronter les modèles probabilistes proposés à ceux de l état de l art.In this thesis we study the statistical modeling of textured images using multi-scale and multi-orientation representations. Based on the results of studies in neuroscience assimilating the human perception mechanism to a selective spatial frequency scheme, we propose to characterize textures by probabilistic models of subband coefficients.Our contributions in this context consist firstly in the proposition of probabilistic models taking into account the leptokurtic nature and the asymmetry of the marginal distributions associated with a textured content. First, to model analytically the marginal statistics of subbands, we introduce the asymmetric generalized Gaussian model. Second, we propose two families of multivariate models to take into account the dependencies between subbands coefficients. The first family includes the spherically invariant processes that we characterize using Weibull distribution. The second family is this of copula based multivariate models. After determination of the copula characterizing the dependence structure adapted to the texture, we propose a multivariate extension of the asymmetric generalized Gaussian distribution using Gaussian copula. All proposed models are compared quantitatively using both univariate and multivariate statistical goodness of fit tests. Finally, the last part of our study concerns the experimental validation of the performance of proposed models through texture based image retrieval. To do this, we derive closed-form metrics measuring the similarity between probabilistic models introduced, which we believe is the third contribution of this work. A comparative study is conducted to compare the proposed probabilistic models to those of the state-of-the-art.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Sistema integrado de modelação para apoio à prevenção e mitigação de acidentes de hidrocarbonetos em estuários e orla costeira

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Oceanografia), Universidade de Lisboa, Faculdade de Ciências, 2010A temática dos derrames de hidrocarbonetos continua a ser um assunto de extrema importância dados os graves impactes continuadamente causados no meio marinho. O desenvolvimento de ferramentas para auxilio às autoridades responsáveis pela gestão costeira no combate e mitigação deste tipo de acidentes é prioritário nos planos estratégicos governamentais. Neste trabalho propõe-se um novo sistema integrado de análise de hidrocarbonetos desenvolvido para aplicação a derrames em zonas costeiras, portuárias e oceânicas. Desenvolveuse uma nova metodologia baseada na sinergia da modelação numérica com a detecção remota por satélites. Este sistema integra uma componente de modelação numérica flexível (2D/3D) e um novo algoritmo de segmentação de manchas de hidrocarbonetos observadas em imagens SAR. O algoritmo de detecção remota foi concebido para validar os resultados do sistema de modelos. O sistema de modelação baseia-se numa abordagem Euleriana- Lagrangeana para a resolução dos processos de evolução dos hidrocarbonetos, utilizando malhas não-estruturadas para a representação dos domínios de estudo numa perspectiva multi-escala. O modelo de hidrocarbonetos inclui a maioria dos processos relevantes num derrame à superfície e na coluna de água e inclui um novo algoritmo para a retenção costeira que considera a dinâmica intertidal para aplicação a domínios praias, lagunas e estuários. Realizaram-se diversas aplicações sintéticas e reais que comprovaram a precisão, robustez, fiabilidade e flexibilidade do sistema integrado, com custos computacionais e níveis de complexidade variável. A aplicação do sistema ao caso Prestige permitiu demonstrar que a sinergia entre a modelação numérica e a detecção remota é uma mais-valia para a previsão de derrames de hidrocarbonetos no mar e serviu como base para uma análise qualitativa da influência da precisão da previsão do vento como agente forçador. Futuramente, o sistema poderá ser aplicado na optimização de planos de contingência, sustentando análises de risco, e em sistemas de alerta e aviso para acidentes de poluição.The severe impact of oil spill accidents in the marine environment reinforces the great importance of these environmental catastrophes. The development of tools to assist coastal management authorities in the prevention and mitigation of such accidents remains a priority in governmental strategic plans. This work proposes a new integrated system of analysis developed for application to oil spills in coastal areas, harbours and ocean. A new methodology was developed based on the synergy of numerical modeling with satellite remote sensing. The proposed system integrates a flexible component of numerical modeling (2D/3D) and a new segmentation algorithm for hydrocarbons spills observed in SAR images. The remote sensing algorithm was designed to validate the results of the modeling system. The modeling system is based on an Eulerian-Lagrangian approach for solving the oil spill processes, using unstructured grids for the discretization of the study area in a multi-scale perspective. Most processes occurring at the sea surface and in the water column during an oil spill are included in the integrated model. A new algorithm for costal retention that considers the intertidal dynamics for application in beaches, coastal lagoons and estuaries was also developed. Several synthetic and real applications were performed to verify the accuracy, robustness, reliability and flexibility of the integrated models, with varying computational costs and degrees of complexity. The application of the methodology to the Prestige accident demonstrated that the synergy between remote sensing and numerical modeling is an asset to the prediction of oil spills fate in the marine environment, and was the basis for a qualitative analysis of the wind forcings accuracy e ect. In the future, the new system can be applied in the prevention, prediction and optimization of contingency plans, supporting risk analysis studies, and as a key element in alert and warning systems for coastal pollution accidents.Fundação para a Ciência e a Tecnologia (SFRH/BD/22124/2005); Laboratório Nacional de Engenharia Civil pela cedência de todos os recursos indispensáveis à realização deste trabalho (destacando os projectos "G-Cast: Aplicação da computação GRID num sistema de simulação e previsão da morfodinâmica em zonas costeiras"( GRID/GRI/81733/2006) e "Distribuição e paralelização de modelos numéricos em Hidráulica e Ambiente" pela disponibilização de formação e acesso aos recursos de HPC

Improved Texture Feature Extraction and Selection Methods for Image Classification Applications

Classification is an important process in image processing applications, and image texture is the preferable source of information in images classification, especially in the context of real-world applications. However, the output of a typical texture feature descriptor often does not represent a wide range of different texture characteristics. Many research studies have contributed different descriptors to improve the extraction of features from texture. Among the various descriptors, the Local Binary Patterns (LBP) descriptor produces powerful information from texture by simple comparison between a central pixel and its neighbour pixels. In addition, to obtain sufficient information from texture, many research studies have proposed solutions based on combining complementary features together. Although feature-level fusion produces satisfactory results for certain applications, it suffers from an inherent and well-known problem called “the curse of dimensionality’’. Feature selection deals with this problem effectively by reducing the feature dimensions and selecting only the relevant features. However, large feature spaces often make the process of seeking optimum features complicated. This research introduces improved feature extraction methods by adopting a new approach based on new texture descriptors called Local Zone Binary Patterns (LZBP) and Local Multiple Patterns (LMP), which are both based on the LBP descriptor. The produced feature descriptors are combined with other complementary features to yield a unified vector. Furthermore, the combined features are processed by a new hybrid selection approach based on the Artificial Bee Colony and Neighbourhood Rough Set (ABC-NRS) to efficiently reduce the dimensionality of the resulting features from the feature fusion stage. Comprehensive experimental testing and evaluation is carried out for different components of the proposed approach, and the novelty and limitation of the proposed approach have been demonstrated. The results of the evaluation prove the ability of the LZBP and LMP texture descriptors in improving feature extraction compared to the conventional LBP descriptor. In addition, the use of the hybrid ABC-NRS selection method on the proposed combined features is shown to improve the classification performance while achieving the shortest feature length. The overall proposed approach is demonstrated to provide improved texture-based image classification performance compared to previous methods using benchmarks based on outdoor scene images. These research contributions thus represent significant advances in the field of texture-based image classification