Corina da Costa Freitas

National Institute for Space Research, Brazil, São José dos Campos, São Paulo, Brazil

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Publications (156)89.54 Total impact

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    ABSTRACT: Of all the parasitic diseases that affect humans, schistosomiasis is one of the most widespread. Considered a serious public health problem, the disease affects thousands of people in Brazil. Since the implementation of schistosomiasis control program in the state of Minas Gerais, stock control and surveillance have been conducted. To contribute to the control and mapping of endemic areas, the aim of this study is to obtain thematic maps showing the risk factor for schistosomiasis mansoni in Minas Gerais. Schistosomiasis is a disease caused by a worm that uses a snail as intermediary host. The worm uses the water to go from the snail to humans. Several variables can contribute for a high risk of a population contracting the disease. In this study, this risk is evaluated from climate, socioeconomic and remote sensing variables, which include MODIS and SRTM data. In this work, two pattern recognition techniques were used to generate two risk maps, with several parameter configurations. The first one is decision trees, for which a total of 19 classifications were generated. The second one technique is the nearest neighbour classification. For this method, only the number of neighbours varied, and 11 classifications were generated. Results showed a better result for the decistion trees in most part of the tests.
    Anais XVII Simpósio Brasileiro de Sensoriamento Remoto - SBSR, João Pessoa-PB, Brasil; 04/2015
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    F. Fonseca · C. Freitas · L. Dutra · R. Guimarães · O. Carvalho
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    ABSTRACT: Schistosomiasis is a transmissible parasitic disease caused by the etiologic agent Schistosoma mansoni, whose intermediate hosts are snails of the genus Biomphalaria. The main goal of this paper is to estimate the prevalence of schistosomiasis in Minas Gerais State in Brazil using spatial disease information derived from the state transportation network of roads and rivers. The spatial information was incorporated in two ways: by introducing new variables that carry spatial neighborhood information and by using spatial regression models. Climate, socioeconomic and environmental variables were also used as co-variables to build models and use them to estimate a risk map for the whole state of Minas Gerais. The results show that the models constructed from the spatial regression produced a better fit, providing smaller root mean square error (RMSE) values. When no spatial information was used, the RMSE for the whole state of Minas Gerais reached 9.5%; with spatial regression, the RMSE reaches 8.8% (when the new variables are added to the model) and 8.5% (with the use of spatial regression). Variables representing vegetation, temperature, precipitation, topography, sanitation and human development indexes were important in explaining the spread of disease and identified certain conditions that are favorable for disease development. The use of spatial regression for the network of roads and rivers produced meaningful results for health management procedures and directing activities, enabling better detection of disease risk areas.
    Acta tropica 05/2014; 133. DOI:10.1016/j.actatropica.2014.01.015 · 2.52 Impact Factor
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    ABSTRACT: The combination of polarimetric SAR (PolSAR) with the interferometry capability (InSAR) enables the extraction of new features that enhances the development of biomass estimation models. This work aims in demonstrating the importance of the simultaneous use of several types of SAR features for estimating forest biomass. The study site is São Gabriel da Cachoeira, located in the Brazilian Amazon. Forest inventory was conducted by INPA and a sample of 29 plots have been used to compute above and below-ground biomass. Polarimetric interferometric X and P band SAR data (PolInSAR) were acquired by the DSG's Amazon Radiography Project. After the initial analysis of the data and feature extraction, analyses on the relationship between biomass and SAR features have been done. Only 4 out of the 122 features extracted presented a significant correlation with biomass and each of them were related to a structural characteristic of the forest.
    IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium; 07/2013
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    ABSTRACT: This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data. This article also proposes to apply the principal component (PC) technique to the multipolarized synthetic aperture radar (SAR), prior to the optical and radar data PC fusion process, aiming at the use of all available polarized information in the fusion process. Although the fused images improve the visual interpretation of the land use classes, the best results are achieved with the simple combination of the Advanced Land Observing Satellite (ALOS)/phased array L-Band SAR (PALSAR) with the LANDSAT5/Thematic Mapper (TM) images. Radar information is found to be particularly useful for improving the user accuracies (UAs) of Soybean with 40 days after seeding (an increase of about 55%), Dirty Pasture (22%), Degraded Forest and Regeneration (5%), and the producer accuracies (PAs) of Clean Pasture (39%), Fallow Agriculture (16%), Degraded Forest and Regeneration (3%), and Primary Forest (2%). Information from the HH (horizontal transmit and horizontal receive) polarization contributes more than that from HV (horizontal transmit and vertical receive) polarization to discriminate the classes, although the use of both polarizations produces results that are statistically better than those obtained with a single polarization.
    GIScience & Remote Sensing 06/2013; 50(3):301-321. DOI:10.1080/15481603.2013.805589 · 1.77 Impact Factor
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    W.B. Silva · Corina C. Freitas · S.J.S. Sant'Anna · A.C. Frery
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    ABSTRACT: A new classifier for Polarimetric SAR (PolSAR) images is proposed and assessed in this paper. Its input consists of segments, and each one is assigned the class which minimizes a stochastic distance. Assuming the complex Wishart model, several stochastic distances are obtained from the h - φ family of divergences, and they are employed to derive hypothesis test statistics that are also used in the classification process. This article also presents, as a novelty, analytic expressions for the test statistics based on the following stochastic distances between complex Wishart models: Kullback-Leibler, Bhattacharyya, Hellinger, Rényi, and Chi-Square; also, the test statistic based on the Bhattacharyya distance between multivariate Gaussian distributions is presented. The classifier performance is evaluated using simulated and real PolSAR data. The simulated data are based on the complex Wishart model, aiming at the analysis of the proposal with controlled data. The real data refer to a complex L-band image, acquired during the 1994 SIR-C mission. The results of the proposed classifier are compared with those obtained by a Wishart per-pixel/contextual classifier, and we show the better performance of the region-based classification. The influence of the statistical modeling is assessed by comparing the results using the Bhattacharyya distance between multivariate Gaussian distributions for amplitude data. The results with simulated data indicate that the proposed classification method has very good performance when the data follow the Wishart model. The proposed classifier also performs better than the per-pixel/contextual classifier and the Bhattacharyya Gaussian distance using SIR-C PolSAR data.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 06/2013; 6(3):1263-1273. DOI:10.1109/JSTARS.2013.2248132 · 3.03 Impact Factor
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    ABSTRACT: This study evaluates the potential of C- and L-band polarimetric SAR data for the discrimination of iron-mineralized laterites in the Brazilian Amazon region. The study area is the N1 plateau located on the northern border of the Carajas Mineral Province, the most important Brazilian mineral province which has numerous mineral deposits, particularly the world's largest iron deposits. The plateau is covered by low-density savanna-type vegetation (campus rupestres) which contrasts visibly with the dense equatorial forest. The laterites are subdivided into three units: chemical crust, iron-ore duricrust, and hematite, of which only the latter two are of economic interest. Full polarimetric data from the airborne R99B sensor of the SIVAM/CENSIPAM (L-band) system and the RADARSAT-2 satellite (C-band) were evaluated. The study focused on an assessment of distinct schemes for digital classification based on decomposition theory and hybrid approach, which incorporates statistical analysis as input data derived from the target decomposition modeling. The results indicated that the polarimetric classifications presented a poor performance, with global Kappa values below 0.20. The accuracy for the identification of units of economic interest varied from 55% to 89%, albeit with high commission error values. In addition, the results using L-band were considered superior compared to C-band, which suggest that the roughness scale for laterite discrimination in the area is nearer to L than to C-band.
    Remote Sensing 06/2013; 5(6):3101-3122. DOI:10.3390/rs5063101 · 3.18 Impact Factor
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    Leonardo Torres · Sidnei J. S. Sant'Anna · Corina da Costa Freitas · Alejandro C. Frery
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    ABSTRACT: This paper presents a technique for reducing speckle in Polarimetric Synthetic Aperture Radar (PolSAR) imagery using Nonlocal Means and a statistical test based on stochastic divergences. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The weights of the location-variant linear filter are function of the p-values of tests which verify the hypothesis that two samples come from the same distribution and, therefore, can be used to compute a local mean. The test stems from the family of (h-phi) divergences which originated in Information Theory. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets.
    Pattern Recognition 04/2013; 47(1). DOI:10.1016/j.patcog.2013.04.001 · 2.58 Impact Factor
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    ABSTRACT: Available from: http://www.intechopen.com/books/parasitic-diseases-schistosomiasis/multiple-regression-for-the-schistosomiasis-positivity-index-estimates-in-the-minas-gerais-state-bra
    Parasitic Diseases - Schistosomiasis, Edited by Prof. Rashika El Ridi (Ed, 01/2013: pages 23; InTech., ISBN: 978-953-51-0942-6
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    ABSTRACT: Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.
    Pesquisa Agropecuária Brasileira 09/2012; 47(9). DOI:10.1590/S0100-204X2012000900004 · 0.68 Impact Factor
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    ABSTRACT: This work presents a region based classifier for Polarimetric SAR (PolSAR) images. The classifier uses the stochastic distances derived from the complex Wishart Model, obtained from the h-φ family of divergences. Adittionaly, a hypothesis test derived from the stochastic distance is also employed in the classification process. The region based classifier, using the Bhattacharyya distance, was applied to a polarimetric SIR-C image from an agricultural area in northeastern Brazil. The region based classification result significantly overperformed the a pixel based/contextual PolSAR classification based on the Maximum Likelihood/Iterated Conditional Modes. Such evidence lead us to conclude that the region based stochastic distance and hypothesis test classifier offers a good potential at identifying the land cover classes on a PolSAR image.
    2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS); 07/2012
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    ABSTRACT: After reviewing some classical statistical hypothesis commonly used in image processing and analysis, this paper presents some statistical properties of synthetic aperture radar (SAR) data. The main focus is on how these specific hypothesis deviate from the classical ones, and on the impact these deviations have on processing and analysis techniques. The multiplicative model, an important tool for SAR data modeling and analysis, is recalled. The work is more focused in the ideas than on equations. A selection of books and papers is collected, aiming at presenting some bibliographic references for the interested reader.
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    ABSTRACT: The impact of intestinal helminths on human health is well known among the population and health authorities because of their wide geographic distribution and the serious problems they cause. Geohelminths are highly prevalent and have a big impact on public health, mainly in underdeveloped and developing countries. Geohelminths are responsible for the high levels of debility found in the younger population and are often related to cases of chronic diarrhea and malnutrition, which put the physical and intellectual development of children at risk. These geohelminths have not been sufficiently studied. One obstacle in implementing a control program is the lack of knowledge of the prevalence and geographical distribution. Geographical information systems (GIS) and remote sensing (RS) have been utilized to improve understanding of infectious disease distribution and climatic patterns. In this study, GIS and RS technologies, as well as meteorological, social, and environmental variables were utilized for the modeling and prediction of ascariasis and trichuriasis. The GIS and RS technologies specifically used were those produced by orbital sensing including the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). The results of this study demonstrated important factors related to the transmission of ascariasis and trichuriasis and confirmed the key association between environmental variables and the poverty index, which enabled us to identify priority areas for intervention planning in the state of Minas Gerais in Brazil.
    Acta tropica 02/2012; 121(2):112-7. DOI:10.1016/j.actatropica.2011.10.011 · 2.52 Impact Factor
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    ABSTRACT: Available from: http://www.intechopen.com/books/schistosomiasis/a-study-of-schistosomiasis-prevalence-and-risk-of-snail-presence-spatial-distributions-using-geo-sta
    Schistosomiasis, Edited by Prof. Mohammad Bagher Rokni, 01/2012: chapter 13: pages 255-280; InTech., ISBN: 978-953-307-852-6
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    ABSTRACT: Geographic Information Systems (GISs) are composed of useful tools to map and to model the spatial distribution of events that have geographic importance as schistosomiasis. This paper is a review of the use the indicator kriging, implemented on the Georeferenced Information Processing System (SPRING) to make inferences about the prevalence of schistosomiasis and the presence of the species of Biomphalaria, intermediate hosts of Schistosoma mansoni, in areas without this information, in the Minas Gerais State, Brazil. The results were two maps. The first one was a map of Biomphalaria species, and the second was a new map of estimated prevalence of schistosomiasis. The obtained results showed that the indicator kriging can be used to better allocate resources for study and control of schistosomiasis in areas with transmission or the possibility of disease transmission.
    Journal of Tropical Medicine 01/2012; 2012:837428. DOI:10.1155/2012/837428
  • F.R. Fonseca · C.C. Freitas · L.V. Dutra
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    ABSTRACT: The main goal of this paper is to develop spatial regression models to estimate the prevalence of schistosomiasis in the state of Minas Gerais, Brazil, using information on the disease spatial dependence through the network of roads and rivers, in addition to climate, socioeconomic and environmental variables. The results showed that the Schistosoma mansoni hosts mobility is an important factor for modeling and estimating the schistosomiasis prevalence distribution. Variables representing vegetation, temperature, precipitation, topography, sanitation and human development indexes, proved their importance in explaining the disease spreading, indicating favorable conditions for the disease development. The use of spatial regression showed meaningful results to the health management procedures and direction of activities, enabling a better detection of disease risk areas.
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
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    Dengsheng Lu · Guiying Li · Emilio Moran · Mateus Batistella · Corina C. Freitas
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    ABSTRACT: This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method.Highlights► We examined the role of different radar data in improving impervious surface mapping. ► High spatial resolution data is needed for accurate spatial patterns of impervious surface. ► Data fusion of TM and high resolution radar data provided better mapping performance.
    ISPRS Journal of Photogrammetry and Remote Sensing 11/2011; 66(6):798-808. DOI:10.1016/j.isprsjprs.2011.08.004 · 3.13 Impact Factor
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    ABSTRACT: Schistosomiasis mansoni is endemic in 54 countries in the Americas and Africa. In Brazil, it is estimated that 4 to 6 million people are infected. Despite advances in knowledge in the field of schistosomiasis, the disease remains a serious public health problem in the country, requiring greater investments in preventive measures such as sanitation and health education. The use of Geographic Information Systems (GIS) tools help to map and understand the spatial distribution of the disease. This study used the geostatistical tool (kriging) of the Georeferenced Information Processing System (SPRING) to estimate the prevalence of schistosomiasis in areas without information. The resulting map was a regular grid of 1 km in the endemic area. The average prevalence per municipality was estimated by kriging and it was compared with the prevalence of Brazilian Schistosomiasis Control Program (PCE) in 255 municipalities. With a significance level of 0.05, it was found that there is no significant difference between the average prevalence estimated and obtained. The kriging can be used to estimate the prevalence of schistosomiasis in the municipalities of the State of Minas Gerais where the prevalence was not determined by the PCE. The results of this tool can be used to better allocate resources for studies in areas with medium and high prevalence. Palavras-chave: GIS, schistosomiasis, krig, SIG, esquistossomose, krigeagem.
    XV Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Curitiba, PR; 04/2011
  • Tessio Novack · Hermann Kux · Corina Freitas
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    ABSTRACT: Assuming that urban planning aims the optimization of urban functioning and the well-being of citizens, questions like “how many people are living in the city?” and “where do they live?” become key issues. In this work we utilized landscape metrics generated by the FragStats software for the estimation of population density out of census sectors in the mega city of São Paulo, Brazil. The metrics were calculated over an image from the QuickBird II sensor classified by the Maximum Likelihood algorithm. The accuracy of the classified image was analyzed qualitatively. Ordinary linear regression models were generated and formal statistical tests applied. The residuals from each model had its spatial dependency analyzed by visualizing its LISA Maps and by the Global Moran index. Afterwards, spatial regression models were tried and a significant improvement was obtained in terms of spatial dependency reduction and increase of the prediction power of the models. For the sake of comparison, the use of dummy variables was also tried and it became a suitable option for eliminating spatial dependency of the residuals as well. The results proved that some landscape metrics obtained over high resolution images, classified by simple supervised methods, can predict well the population density at the area under study when using it as independent variable in spatial regression models. KeywordsPopulation density–Spatial regression–High resolution remote sensing
    03/2011: pages 111-122;
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    ABSTRACT: The increasing practice of ecotourism and rural tourism in the State of Minas Gerais, Brazil, highlights the importance of studies concerning the occurrence of potential intermediate hosts of Schistosoma mansoni. This study aimed to identify species of Biomphalaria snails in municipalities along the Estrada Real, an important Brazilian tourism project. The specimens were collected in different water collections of 36 municipalities along the Estrada Real in the southeast of the State of Minas Gerais. Biomphalaria species were characterized using both morphological and molecular approaches. The research was conducted between August 2005 and September 2009 and all the sites visited were georeferenced using GPS. Six Biomphalaria species were found in 30 of the 36 municipalities studied: glabrata, tenagophila, straminea, peregrina, occidentalis and schrammi. The first three species of Biomphalaria, recognized as intermediate hosts of S. mansoni, were present in 33.3%, 47.2% and 8.3% of the municipalities studied, respectively. The mollusks were found in different types of water collections and no infection by S. mansoni was detected. The highest occurrence of Biomphalaria concentration was verified in the area covered by the Caminho Novo route (Diamantina/MG to Rio de Janeiro/RJ). Considering the occurrence of schistosomiasis in the State of Minas Gerais and the socioeconomic repercussions involved in the Estrada Real Project, this work focuses on the vulnerability of water collections due to the presence of Biomphalaria mollusks and emphasizes the need for epidemiological surveillance and sanitary and educational measures integrated with the local community and tourism sectors.
    Revista da Sociedade Brasileira de Medicina Tropical 01/2011; 44(2):163-7. DOI:10.1590/S0037-86822011005000005 · 0.94 Impact Factor
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    ABSTRACT: Given the different nature of optical and radar data, it is reasonable the idea that each type of data can contribute in complementary ways for different applications. This paper aims at analyzing the potential joint usage of optical and Synthetic Aperture Radar (SAR) data for land use and land cover classification in a region located in the Brazilian Amazon. To achieve this objective, we evaluated regionbased classifications using separated and fused optical and SAR data. Data were images from the Landsat 5/TM sensor and amplitude multipolarized images from the ALOS/PALSAR sensor. The images were classified using a region-based classifier based on the Bhattacharyya distance between Gaussian distributions. The TM data alone is better for classify land cover classes with occurrence of trees or shrubs, while SAR data contribute to improve the classification results in low vegetated areas.
    2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, Canada, July 24-29, 2011; 01/2011

Publication Stats

801 Citations
89.54 Total Impact Points

Institutions

  • 1998–2014
    • National Institute for Space Research, Brazil
      • • Divisão de Processamento de Imagens (DPI)
      • • Remote Sensing Division
      São José dos Campos, São Paulo, Brazil
  • 1999–2013
    • Fatec Sao Jose dos Campos
      São José, Santa Catarina, Brazil
    • The University of Sheffield
      Sheffield, England, United Kingdom