Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty

In this study, a segmentation procedure is proposed, based on grey-level and multivariate texture to extract spatial objects from an image scene. Object uncertainty was quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, was integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image. We proposed a multivariate extension of the standard univariate LBP operator to describe colour texture. The paper is illustrated with two case studies. The first considers an image with a composite of texture regions. The two LBP operators provided good segmentation results on both grey-scale and colour textures, depicted by accuracy values of 96% and 98%, respectively. The second case study involved segmentation of coastal land cover objects from a multi-spectral Compact Airborne Spectral Imager (CASI) image, of a coastal area in the UK. Segmentation based on the univariate LBP measure provided unsatisfactory segmentation results from a single CASI band (70% accuracy). A multivariate LBP-based segmentation of three CASI bands improved segmentation results considerably (77% accuracy). Uncertainty values for object building blocks provided valuable information for identification of object transition zones. We conclude that the (multivariate) LBP texture model in combination with a hierarchical splitting segmentation framework is suitable for identifying objects and for quantifying their uncertainty

Region-shrinking: a hybrid segmentation technique for isolating continuous features, the case of oceanic eddy detection

In remote sensing image analysis, limited attention has been devoted to the isolation of fuzzy objects, namely those with inherently indeterminate boundaries, from continuous field data. This study develops a “region-shrinking” segmentation technique tailored specifically to the problem of fuzzy object identification, and applies it to the test case of oceanic eddy detection. The region-shrinking technique employs an evolutionary boundary definition procedure to simultaneously identify segments containing oceanic eddies, and refine the boundaries of these segments to correspond to eddy perimeters. This algorithm combines a Non-Euclidean Voronoi segmentation technique with insights from existing work on eddy detection from an oceanographic perspective, with the goal of improving the detection of eddy features through sea level anomaly imagery. It takes into account multiple criteria (vorticity, size, amplitude, the existence of local sea level extrema, concave or convex shape) to iteratively refine the identification and demarcation of oceanic eddies. The resulting polygons define tightly fitted eddy boundaries, and conform to the key rotational and cross-sectional height profile characteristics used by physical oceanographers to identify eddies

Comparing global land cover datasets through the Eagle matrix land cover components for continental Portugal

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesGlobal land cover maps play an important role in the understanding of the Earth's ecosystem dynamic. Several global land cover maps have been produced recently namely, Global Land Cover Share (GLC-Share) and GlobeLand30. These datasets are very useful sources of land cover information and potential users and producers are many times interested in comparing these datasets. However these global land cover maps are produced based on different techniques and using different classification schemes making their interoperability in a standardized way a challenge. The Environmental Information and Observation Network (EIONET) Action Group on Land Monitoring in Europe (EAGLE) concept was developed in order to translate the differences in the classification schemes into a standardized format which allows a comparison between class definitions. This is done by elaborating an EAGLE matrix for each classification scheme, where a bar code is assigned to each class definition that compose a certain land cover class. Ahlqvist (2005) developed an overlap metric to cope with semantic uncertainty of geographical concepts, providing this way a measure of how geographical concepts are more related to each other. In this paper, the comparison of global land cover datasets is done by translating each land cover legend into the EAGLE bar coding for the Land Cover Components of the EAGLE matrix. The bar coding values assigned to each class definition are transformed in a fuzzy function that is used to compute the overlap metric proposed by Ahlqvist (2005) and overlap matrices between land cover legends are elaborated. The overlap matrices allow the semantic comparison between the classification schemes of each global land cover map. The proposed methodology is tested on a case study where the overlap metric proposed by Ahlqvist (2005) is computed in the comparison of two global land cover maps for Continental Portugal. The study resulted with the overlap spatial distribution among the two global land cover maps, Globeland30 and GLC-Share. These results shows that Globeland30 product overlap with a degree of 77% with GLC-Share product in Continental Portugal

Impact of Sea Level Rise on Development Suitability in New York City

Global climate change and resultant rising sea levels and more frequent flooding are impacting the sustainability and vitality of coastal communities, making identifying vulnerable areas particularly important. Sea level rise projections for the 2020s and 2050s were incorporated into a land use suitability analysis of New York City, which was conducted in a GIS environment based on Ian McHarg’s overlay methods. The analytical hierarchy process (AHP) was used to produce weights for the six criteria considered, which were then reclassified and combined according to a weighted linear combination. The results of the suitability analysis suggest that Eastern Staten Island and the southern shore of Brooklyn and Queens are particularly unsuitable for future development. This analysis could be improved by better considering hydrological connectivity when modeling sea level rise.Bachelor of Scienc

Análise da acurácia temática das classificações de Imagens Orbitais AVNIR-2/ALOS, CCD/CBERS-2 e TM/LANDSAT-5, comparando as abordagens de Máxima Verossimilhança e Fuzzy.

A utilização de classificações digitais no mapeamento da cobertura do solo de bacias hidrográficas permite estudos e planejamento de atividades urbanas e rurais, indicando áreas propícias à exploração agrícola, pecuária ou florestal. Assim, estimar a acurácia de um mapa de cobertura do solo é fundamental para que este seja utilizado adequadamente. Este trabalho analisa área piloto localizada na bacia hidrográfica do município de São Carlos/SP (com aproximadamente 1,6 km2 e 5,6 km de perímetro), através de mapeamentos de cobertura do solo obtidos pelos classificadores digitais Máxima Verossimilhança e Fuzzy. Analisa ainda a influência da resolução espacial nos mapeamentos de cobertura do solo, fazendo uso de cenas dos sensores AVNIR-2/ALOS, CCD/CBERS-2 e TM/LANDSAT-5, com resolução espacial de 10, 20 e 30m respectivamente. Primeiramente foram identificados em mosaico de fotografias aéreas coloridas oito tipos de cobertura do solo, os quais foram digitalizados e convertidos em imagens (rasterizacao) para compor mapas de verdade terrestre para servirem de parâmetros na comparação com os resultados das classificações digitais. Para tanto, em seguida, foram aplicados os classificadores digitais Máxima Verossimilhança e Fuzzy nas cenas dos sensores citados anteriormente. As comparações dos mapas resultantes das classificações com suas respectivas verdades terrestres foram feitas via matrizes do erro e Índices Kappa. Em relação aos Índices Kappa, encontrou-se para a classificação Máxima Verossimilhança 0,4688; 0,5139 e 0,3144 (AVNIR-2/ALOS, CCD/CBERS-2 e TM/LANDSAT-5, respectivamente). Para a classificação Fuzzy obteve-se 0,5418; 0,5332 e 0,3927 (AVNIR-2/ALOS, CCD/CBERS-2 e TM/LANDSAT-5, respectivamente). Houve, portanto, uma melhora de 7,3% quando se aplicou o classificador Fuzzy nas imagens do AVNIR-2/ALOS, 2% no caso das imagens do CCD/CBERS-2 e 3% no caso das classificações provenientes das imagens do TM/LANDSAT-5. Pode-se concluir, considerando apenas a resolução espacial dos sensores, que os resultados correspondentes ao sensor AVNIR-2/ALOS apresentam melhor qualidade, porém o emprego do sensor CCD/CBERS-2 oferece a melhor relação custo/benefício, uma vez que, diferentemente das cenas do AVNIR-2/ALOS, as imagens do CCD/CBERS-2 são disponíveis gratuitamente na rede (assim como as do TM/LANDSAT-5). Verifica-se a relação direta entre acurácia e resolução espacial dos sensores, comprovando-se que a capacidade de um sensor em discernir alvos espectrais é acrescida com o aumento da resolução espacial. Conclui-se também que a qualidade do mapeamento foi superior quando aplicado o classificador Fuzzy, diminuindo consideravelmente o efeito de borda ocorrido pela existência de pixels mistos, dentre outras confusões. A qualidade da classificação (acurácia) pode ser comprometida pelas áreas de transição, muitas vezes abruptas, entre os temas e por variações nas respostas espectrais dos alvos, como a quantidade de água no solo ou na vegetação em diferentes épocas do ano, como ocorreu neste trabalho

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study

Formalizing fuzzy objects from uncertain classification results

Concepts of fuzzy objects have been put forward by various authors (Burrough and Frank 1996) to represent objects with indeterminate boundaries. In most of these proposals the uncertainties in thematic aspects and the geometric aspects are treated separately. Furthermore little attention is paid to methods for object identification, whereas it is generally in this stage that the uncertainty aspects of objects become manifest. When objects are to be extracted from image data then the uncertainty of image classes will directly effect the uncertainty of the determination of the spatial extent of objects. Therefore a complete and formalized description of fuzzy objects is needed to integrate these two aspects and analyse their mutual effects. The syntax for fuzzy objects (Molenaar 1998), was developed as a generalization of the formal syntax model for conventional crisp objects by incorporating uncertainties. This provides the basic framework for the approach presented in this paper. However, the model still needs further development in order to represent objects for different application contexts. Moreover, the model needs to be tested in practice. This paper proposes three fuzzy object models to represent objects with fuzzy spatial extents for different situations. The Fuzzy-Fuzzy object (FF-object) model represents objects that have an uncertain thematic description and an uncertain spatial extent, these objects may spatially overlap each other. The Fuzzy-Crisp object (FC-object) model represents objects with an uncertain spatial extent but a determined thematic content and the Crisp-Fuzzy object (CF-object) model represents objects with a crisp boundary but uncertain content. The latter two models are suitable for representing fuzzy objects that are spatially disjoint. The procedure and criteria for identifying the conditional spatial extent and boundaries based upon fuzzy classification result are discussed and are formalized based upon the syntactic representation. The identification of objects by these models is illustrated by two cases: One from coastal geomorphology of Ameland, The Netherlands and one from land cover classification of Hong Kong