SEGMENTAÇÃO DE DADOS DE PERFILAMENTO A LASER EM ÁREAS URBANAS UTILIZANDO UMA ABORDAGEM BAYESIANA

In this paper is presented a region-based methodology for Digital Elevation Modelsegmentation obtained from laser scanning data. The methodology is based on twosequential techniques, i.e., a recursive splitting technique using the quad treestructure followed by a region merging technique using the Markov Random Fieldmodel. The recursive splitting technique starts splitting the Digital Elevation Modelinto homogeneous regions. However, due to slight height differences in the DigitalElevation Model, region fragmentation can be relatively high. In order to minimizethe fragmentation, a region merging technique based on the Markov Random Fieldmodel is applied to the previously segmented data. The resulting regions are firstlystructured by using the so-called Region Adjacency Graph. Each node of theRegion Adjacency Graph represents a region of the Digital Elevation Modelsegmented and two nodes have connectivity between them if corresponding regionsshare a common boundary. Next it is assumed that the random variable related toeach node, follows the Markov Random Field model. This hypothesis allows thederivation of the posteriori probability distribution function whose solution isobtained by the Maximum a Posteriori estimation. Regions presenting highprobability of similarity are merged. Experiments carried out with laser scanningdata showed that the methodology allows to separate the objects in the DigitalElevation Model with a low amount of fragmentation.Neste artigo é apresentada uma metodologia para a segmentação de um ModeloDigital de Elevação obtido a partir de um sistema de varredura a laser. Ametodologia de segmentação baseia-se na utilização das técnicas de divisãorecursiva usando a estrutura quadtree e de fusão de regiões usando o modeloMarkov Random Field. Inicialmente a técnica de divisão recursiva é usada paraparticionar o Modelo Digital de Elevação em regiões homogêneas. No entanto,devido a ligeiras diferenças de altura no Modelo Digital de Elevação, nesta etapa afragmentação das regiões pode ser relativamente alta. Para minimizar essafragmentação, uma técnica de fusão de regiões baseada no modelo Markov RandomField é aplicada nos dados segmentados. As regiões resultantes são estruturadasusando um grafo de regiões adjacentes (Region Adjacency Graph). Cada nó doRegion Adjacency Graph corresponde a uma região do Modelo Digital de Elevaçãosegmentado e dois nós tem conectividade entre eles se as duas regiõescorrespondentes compartilham de uma mesma fronteira. Em seguida, assume-se queo comportamento da variável aleatória em relação a cada nó dá se de acordo comum Markov Random Field. Esta hipótese permite a obtenção da chamadadistribuição de probabilidade a posteriori, cuja solução é obtida via estimativa Maximum a Posteriori. Regiões que apresentam alta probabilidade de fusão sãofundidas. Os experimentos realizados com os dados de perfilamento a lasermostraram que a metodologia proposta permitiu separar os objetos no ModeloDigital de Elevação com um baixo nível de fragmentação

Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm

This work approaches the texture segmentation problem by incorporating Gibbs sampler (i.e., the combination of Markov random fields and simulated annealing) and a region-merging process within a multiresolution structure with "high class resolution and low boundary resolution" at high levels and "low class resolution and high boundary resolution" at lower ones. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class-boundary uncertainty and to improve the segmentation accuracy. The computational complexity and frequent occurrences of over-segmentation of Gibbs sampler are addressed and the computationally and functionally effective region-merging process is included to allow Gibbs sampler to start its annealing schedule at relatively low pseudo-temperature and to guide the search trajectory away from local minima associated with over-segmented configurations

Développement d'une nouvelle approche basée objets pour l'extraction automatique de l'information géographique en milieu urbain à partir des images satellitaires à très haute résolution spatiale

Résumé: L'importance de l'information géographique est indéniable pour des prises de décision efficaces dans le milieu urbain. Toutefois, sa disponibilité n'est pas toujours évidente. Les images satellitaires à très résolution spatiale (THRS) constituent une source intéressante pour l'acquisition de ces informations. Cependant, l'extraction de l'information géographique à partir de ces images reste encore problématique. Elle fait face, d'une part, aux spécificités du milieu urbain et celles des images à THRS et d'autre part, au manque de méthodes d'analyse d'images adéquates. Le but de la présente étude est de développer une nouvelle approche basée objets pour l'extraction automatique de l'information géographique en milieu urbain à partir des images à THRS. L'approche proposée repose sur une analyse d'image basée objets. Deux étapes principales sont identifiées : le passage des pixels aux primitives objets et le passage des primitives aux objets finaux. La première étape est assurée par une nouvelle approche de segmentation multispectrale non paramétrée. Elle se base sur la coopération entre les segmentations par régions et par contours. Elle utilise un critère d'homogénéité spectrale dont le seuil est déterminé d'une manière adaptive et automatique. Le deuxième passage part des primitives objets créées par segmentation. Elle utilise une base de règles floues qui traduisent la connaissance humaine utilisée pour l'interprétation des images. Elles se basent sur les propriétés des objets des classes étudiées. Des connaissances de divers types sont prises en considération (spectrales, texturales, géométriques, contextuelles). Les classes concernées sont : arbre, pelouse, sol nu et eau pour les classes naturelles et bâtiment, route, lot de stationnement pour les classes anthropiques. Des concepts de la théorie de la logique floue et celle des possibilités sont intégrés dans le processus d'extraction. Ils ont permis de gérer la complexité du sujet étudié, de raisonner avec des connaissances imprécises et d'informer sur la précision et la certitude des objets extraits. L'approche basée objets proposée a été appliquée sur des extraits d'images Ikonos et Quickbird. Un taux global de 80 % a été observé. Les taux de bonne extraction trouvés pour les classes bâtiment, route et lots de stationnement sont de l'ordre de 81 %, 75 % et 60 % respectivement. Les résultats atteints sont intéressants du moment que la même base des règles a été utilisée. L'aspect original réside dans le fait que son fonctionnement est totalement automatique et qu'elle ne nécessite ni données auxiliaires ni zones d'entraînement. Tout le long des différentes étapes de l'approche, les paramètres et les seuils nécessaires sont déterminés de manière automatique. L'approche peut être transposable sur d'autres sites d'étude. L'approche proposée dans le cadre de ce travail constitue une solution intéressante pour l'extraction automatique de l'information géographique à partir des images à THRS.||Abstract: The importance of the geographical information is incontestable for efficient decision making in urban environment. But, it is not always available.The very high spatial resolution (VHSR) satellite images constitute an interesting source of this information. However, the extraction of the geographical information from these images is until now problematic.The goal of the present study is to develop a new object-based approach for automatic extraction of geographical information in urban environment from very high spatial resolution images.The proposed approach is object-based image analysis. There are two principal steps: passage of pixels to object primitives and passage of primitives to final objects.The first stage uses a new multispectrale cooperative segmentation approach. Cooperation between region and edge information is exploited. Segments are created with respect to their spectral homogeneity.The threshold is adaptive and its determination is automatic.The second passage leaves from object primitives created by segmentation. Fuzzy rule base is generated from the human knowledge used for image interpretation. Several kinds of object proprieties are integrated (spectral, textural, geometric, and contextual).The concerned classes are trees, grass, bare soil and water as natural classes and building, road, parking lot as man made classes. Fuzzy logic and possibilities theories are integrated in the process of extraction. They permitted to manage the complexity of the studied objects, to reason with imprecise knowledge and to inform on precision and certainty of the extracted objects.The approach has been applied with success on various subsets of Ikonos and Quickbird images.The global extraction accuracy was about 80%.The object-based approach was able to extract buildings, roads and parking lots in urban areas with of 81%, 75% and 60% extraction accuracies respectively.The results are interesting with regard to that the same rule base was used.The original aspect resides in the fact that the approach is completely automatic and no auxiliary data or training areas are required. Along the different stages of the approach, the parameters and the thresholds are determined automatically. This allows the transposability of the approach on others VHRS images.The present approach constitutes an interesting solution for automatic extraction of the geographical information from VHSR satellite images