Resumo em Inglês:

Abstract The extraction of information from point cloud is usually done after the application of classification methods based on the geometric characteristics of the objects. However, the classification of photogrammetric point clouds can be carried out using radiometric information combined with geometric information to minimize possible classification issues. With this in mind, this work proposes an approach to the classification of photogrammetric point cloud, generated by correspondence of aerial images acquired by Remotely Piloted Aircraft System (RPAS). The proposed approach for classifying photogrammetric point clouds consists of a pixel-supervised classification method, based on a decision tree. To achieve this, three data sets were used, one to define which attributes allow discrimination between the classes and the definition of the thresholds. Initially, several attributes were extracted based on a training sample. The average and standard deviation values for the attributes of each class extracted were used to guide the decision tree definition. The defined decision tree was applied to the other two point clouds to validate the approach and for thematic accuracy assessment. The quantitative analyses of the classifications based on kappa coefficient of agreement, applied to both validation areas, reached values higher than 0.938.

Resumo em Inglês:

Abstract Metadata represents a set of descriptive information about the data, which aims at facilitating the search, access and use of data. The metadata standards specify the minimum set of elements to be informed and the file structure to ensure interoperability among catalogs. The present work aims at analyzing the adherence of the metadata published in the Brazilian Spatial Data Infrastructure (SDI) repository to the MGB Profile (in Portuguese, Perfil de Metadados Geoespaciais do Brasil) through a conformity indicator. Conformity was evaluated for over 30,000 metadata inserted in the INDE catalog (in Portuguese, Infraestrutura Nacional de Dados Espaciais), in June 2018, considering the set of mandatory and conditional elements of the summarized MGB Profile. These elements were collected from a harvester elaborated by the authors capable of scanning all the metadata of the repository and returning the fields organized into a CSV file. After analysis, it was found that only 28% of metadata conforms to the summarized MGB Profile. The low conformity rate suggests the limited understanding of the standards recommended, regarding both the minimum elements to be informed about each product and the structuration of the information in XML.

Resumo em Inglês:

Abstract Different built-up structures usually lead to large regions covered by shadows, causing partial or total loss of information present in urban environments. In order to mitigate the presence of shadows while improving the urban target discrimination in multispectral images, this paper proposes an automated methodology for both detection and recovery of shadows. First, the image bands are preprocessed in order to highlight their most relevant parts. Secondly, a shadow detection procedure is performed by using morphological filtering so that a shadow mask is obtained. Finally, the reconstruction of shadow-occluded areas is accomplished by an image inpainting strategy. The experimental evaluation of our methodology was carried out in four study areas acquired from a WorldView-2 (WV-2) satellite scene over the urban area of ​​São Paulo city. The experiments have demonstrated a high performance of the proposed shadow detection scheme, with an average overall accuracy up to 92%. Considering the results obtained by our shadow removal strategy, the pre-selected shadows were substantially recovered, as verified by visual inspections. Comparisons involving both VrNIR-BI and VgNIR-BI spectral indices computed from original and shadow-free images also attest the substantial gain in recovering anthropic targets such as streets, roofs and buildings initially damaged by shadows.

Resumo em Inglês:

Abstract This article has theoretically discussed some points regarding outliers caused by errors in geodetic observations (see consideration made). Comments have also been made on the usual 3σ-rule to identify outliers and its common approachs in the simulation of outliers in geodetic networks. Three simulated experiments have been conducted to verify the elements discussed. In the first one, with the simulation of random errors, we have verified that it can have a magnitude large enough to generate outliers. In the second one, in scenarios of leveling network simulated by Monte Carlo methods, observations containing gross errors with a lower magnitude than their respective σ tended to not be identified as outliers by the iterative data snooping procedure. This has also occurred in the third experiment, in which gross errors of magnitude 3.1σ had their value masked by the random error of the respective observation. From the conceptual discussion presented, we have concluded that gross error and outlier are not synonyms, and neither is one a particular case of the other. From the obtained results, we have concluded that there are inconsistencies in how outliers have been simulated in geodetic networks, which indicates the need to continue with investigations.

Resumo em Inglês:

Abstract Measurements from the Global Navigation Satellite System (GNSS) have become a leading data source for ionospheric studies. Different technologies are used to monitor the ionospheric layer. It is possible to carry out this monitoring using GNSS networks through the indices of ionospheric irregularities, as well as through ionosondes and imagers. It has therefore become essential to correlate these forms of monitoring, presenting their advantages and disadvantages. With this in mind, the aim of this work was to perform an analysis of the behavior of the ionosphere in the Brazilian region through the ionospheric irregularity indices, along with ionosonde information and all-sky optical imagers, for a day of high solar activity (1 March 2014) and a day of low activity (12 April 2015). The results of each monitoring technique were compatible for the different scenarios, showing a moderate and positive correlation between the irregularity indices (FP) and ionosonde parameter. The imagers perform measures of greater spatial extent, however, they need favorable meteorological conditions. The ionosondes present a greater diagnostic capacity of the ionosphere but they are fewer in number than the imagers. The GNSS networks become ionosphere monitoring stations through the irregularity indices, enabling an increase in spatial resolution and a better understanding of ionospheric phenomena in the Brazilian territory.