124 resultados para geodésicas


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Neste artigo é proposto um método semiautomático para extração de rodovias combinando um estereopar de imagens aéreas de baixa resolução com um poliedro gerado a partir de um modelo digital do terreno (MDT). O problema é formulado no espaço-objeto através de uma função objetivo que modela o objeto 'rodovia' como uma curva suave e pertencente a uma superfície poliédrica. A função objetivo proposta depende também de informações radiométricas, que são acessadas no espaço-imagem via relação de colinearidade entre pontos da rodovia no espaço-objeto e os correspondentes nos espaços imagem do estereopar. A linha poligonal que melhor modela a rodovia selecionada é obtida por otimização no espaço-objeto da função objetivo, tendo por base o algoritmo de programação dinâmica. O processo de otimização é iterativo e dependente do fornecimento por um operador de uma aproximação inicial para a rodovia selecionada. Os resultados obtidos mostraram que o método é robusto frente a anomalias existentes ao longo das rodovias, tais como obstruções causadas por sombras e árvores.

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In this paper the theoretical fundamentals related to the introduction of NNT (No Net Translation) and NNR (No Net-Rotation) conditions in the realization of a referential are presented. Moreover, aspects related to the implementation are also presented, with special emphasis on the realization from VLBI data, where only scale information is available.

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One of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces.

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Several kinds of research in road extraction have been carried out in the last 6 years by the Photogrammetry and Computer Vision Research Group (GPF&VC - Grupo de Pesquisa em Fotogrametria e Visão Computacional). Several semi-automatic road extraction methodologies have been developed, including sequential and optimizatin techniques. The GP-F&VC has also been developing fully automatic methodologies for road extraction. This paper presents an overview of the GP-F&VC research in road extraction from digital images, along with examples of results obtained by the developed methodologies.

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The multipath effect affects the differential and relative positioning, even that one involving short baselines. So it is necessary to detect this effect, check the caused error level, and mainly, its removal. This paper aims at analysing and comparing some useful components in the detection of this effect. These components are the Signal to Noise Ratio (SNR), the values of MP1 and MP2 obtained from the TEQC software that indicates the multipath level in the carriers L1 and L2, the multipath repeatability in consecutive days and the elevation angle and the azimuth of the satellites. For this purpose, an experiment is carried out, comparing such components in the presence and the absence of reflector objects that cause the multipath. Not only there is clear multipath repeatability in the residuals, but it also appears in the measures SNR, MP1 and MP2, reaching up 99% of correlation. For reduction, at least, of the high frequency multipath effect, the Multi-Resolution Analysis using wavelets is applied in the double differences (DD) measures. Some statistical tests were accomplished, which indicate results improvement, and mainly, larger reliability in the solution of the ambiguities, reaching up 49% of improvement concerning the Ratio test without applying the proposed method.

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Single frequency GPS receivers have been many used in GPS surveys. Among the several applications, one can mention those that are to obtain the receiver's antenna coordinates in real time. One of the main error sources to these applications is the ionosphere systematic error. In the FCT/UNESP a regional ionosphere model (Mod_Ion) was developed. It has been implemented to execute after collecting of GPS data. At real time application two improvements in the Mod_Ion were introduced, consisting of an alteration of the function of modeling and implementation of the Kalman Filter. The results of the experiments showed that the modifications were the most effective in the ionosphere systematic effect's corrections, providing a improvement in the accuracy of point positioning, of 90,75%, in period of the highest ionosphere activity.

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The article focuses on the presence of the technologic innovation and the surveyed current conditions of the cartographic engineer's work market. The methodology is based on forms with objective questions answered by the professionals. The current survey is the fourth one starting in 1995 and the first one with on line forms filled out by the Brazilian Association of Cartographic Engineers - São Paulo Section's web page. The sample was made up with 141 engineers and it revealed numbers that confirm the growing visibility and social recognition of the Brazilian cartographic engineer, although still far from the condition consistent with the territorial extension and the importance of Cartography for the country. The survey shows that the monthly average revenues are of approximately fifteen minimum wages; that the sample has only 13% of employers, although it is the highest percentage among all surveys; that 63 professionals cited 127 times that their organizations are related to technologic innovation at a proportion of two citations per organization; that 69% of the sample are optimists concerned to the future. Other important pieces of in formation to students and professionals will also be discussed in the article.

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The purpose of this paper is to introduce a methodology for semi-automatic road extraction from aerial digital image pairs by using dynamic programming and epipolar geometry. The method uses both images from where each road feature pair is extracted. The operator identifies the corresponding road featuresand s/he selects sparse seed points along them. After all road pairs have been extracted, epipolar geometry is applied to determine the automatic point-to-point correspondence between each correspondent feature. Finally, each correspondent road pair is georeferenced by photogrammetric intersection. Experiments were made with rural aerial images. The results led to the conclusion that the methodology is robust and efficient, even in the presence of shadows of trees and buildings or other irregularities.

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In this paper is proposed a methodology for semiautomatic CBERS image orientation using roads as ground control. It is based on an iterative strategy involving three steps. In the first step, an operator identifies on the image the ground control roads and supplies along them a few seed points, which could be sparsely and coarsely distributed. These seed points are used by the dynamic programming algorithm for extracting the ground control roads from the image. In the second step, it is established the correspondences between points describing the ground control roads and the corresponding ones extracted from the image. In the last step, the corresponding points are used to orient the CBERS image by using the DLT (Direct Linear Transformation). The two last steps are iterated until the convergence of the orientation process is verified. Experimental results showed that the proposed methodology was efficient with several test images. In all cases the orientation process converged. Moreover, the estimated orientation parameters allowed the registration of check roads with pixel accuracy or better.

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The error associated with the ionosphere depends on Total Electron Content (TEC) of the ionosphere. The geomagnetic field exerts strong influence in the TEC variation, because it controls the movement of the electrons. After solar events the magnetic lines of force can be compressed, characterizing the geomagnetic storm. The aim of this paper is to present to geodesic community the effects of a geomagnetic storm in the relative positioning. The processing of the data was accomplished with an interval of two hours, with a 430 km baseline. The analyze of the obtained results have been carried out from the discrepancies between the true coordinates and corresponding ones obtained in the processing of the baseline. The used data in this paper include the period of 30/03/2001 up to 02/04/2001. In March 31 a strong geomagnetic storm happened. One day after, that it corresponds to main phase of the storm, the values of the discrepancies decreased significantly. For instance, in 01:00-03:00 UT period, the value of the planimetric discrepancy reached 20 m in the storm day. However, in the main phase of the storm, the planimetric discrepancy decreased to 0.1 m.

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The aim of this paper is to present a model for orientation of pushbroom sensors that allows estimating the polynomial coefficients describing the trajectory of the platform, using linear features as ground control. Considering that pushbroom image acquisition is not instantaneous, six EOP (Exterior Orientation Parameters) for each scanned line must be estimated. The sensor position and attitude parameters are modeled with a time dependent polynomial. The relationship between object and image space is established through a mathematical model based on the equivalence between the vector normal to the projection plane in the image space and to the vector normal to the rotated projection plane in the object space. The equivalence property between planes was adapted to consider the pushbroom geometry. Some experiments with simulated data corresponding to CBERS scene (China-Brazil Earth Resource Satellite) were accomplished in order to test the developed model using straight lines. Moreover, experiments with points ground with the model based on collinearity equations adapted to the pushbroom geometry were also accomplished. The obtained results showed that the proposed model can be used to estimate the EOP of pushbroom images with suitable accuracy.

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The mathematical fundamentals involved in the realization of a high precision reference system, taking into account solutions from VLBI, SLR, GPS and DORIS are presented. Each individual solution has its own deficiencies in providing a global reference system. But combining all solutions together, and by the introduction of the no net rotation condition (NNR), a solution that reflects the nowadays precision of the available spatial techniques for the determination of position and velocity of stations located on the Earth crust is obtained. Whilst the origin is provided by SLR, the scale is introduced by SLR and VLBI. The orientation and its temporal evolution are introduced via internal constraints, in accordance with the NNR condition.

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The great diversity of materials that characterizes the urban environment determines a structure of mixed classes in a classification of multiespectral images. In that sense, it is important to define an appropriate classification system using a non parametric classifier, that allows incorporating non spectral (such as texture) data to the process. They also allow analyzing the uncertainty associated to each class from the output alues of the network calculated in relation to each class. Considering these properties, an experiment was carried out. This experiment consisted in the application of an Artificial Neural Network aiming at the classification of the urban land cover of Presidente Prudente and the analysis of the uncertainty in the representation of the mapped thematic classes. The results showed that it is possible to discriminate the variations in the urban land cover through the application of an Artificial Neural Network. It was also possible to visualize the spatial variation of the uncertainty in the attribution of classes of urban land cover from the generated representations. The class characterized by a defined pattern as intermediary related to the impermeability of the urban soil presented larger ambiguity degree and, therefore, larger mixture.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)