[Show abstract][Hide abstract] ABSTRACT: To estimate the schistosomiasis prevalence in the Minas Gerais state, Brazil, using spatial disease information derived from the state transport network derived from roads and rivers.
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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 01/2013; 6(3):1263-1273. · 2.87 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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. · 0.93 Impact Factor
[Show abstract][Hide abstract] 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 01/2011; 66(6):798-808. · 3.31 Impact Factor