[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: 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: 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: 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. · 2.90 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This work presents a comparative analysis of ERS-1 Synthetic Aperture Radar (SAR) and Landsat-5 Thematic Mapper (TM) images used for land use classiécation. The study area of 361 km2 is located in the City of Campinas, Sao Paulo State, Brazil, and contains several classes of land use, includingurban,agriculturaland forests.The TM and SAR images were registered and transformed using the principal components transformation. SAR images were also é ltered using an average é lter. The principal componentsderived from SAR é ltered, SAR, TM and coregistered TM/SAR and TM/SAR é ltered images were classiéed using the maximum likelihood approach. Tests of 'goodness of é t' were also made to assess the statistical properties of the images. The results, conérmed by Kappa statistics, show a signié cant improvement when classifying the principal componentsof é ltered SAR and TM images for urban, pasture and forest classes.
International Journal of Remote Sensing 11/2010; 21(10). · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The use of optical remote sensing data in large tropical forest regions has an important limitation due to cloud cover. Synthetic Aperture Radar (SAR) data can be a viable alternative in areas where cloud cover is permanent, because the data acquisition is independent on atmospheric conditions. In this context, the main objective of this work was to evaluate the potential of L band SAR data acquired by R99B Brazilian Air Force (FAB) airborne system to discriminate deforestation increments in the Amazon rainforest. In order to achieve this purpose, we performed Maximum Likelihood classifications with multipolarized SAR data of a test site located in the state of Acre. The classifications performed with the combination of three channels (HH+HV+VV) and with the polarization pair HH+HV obtained good agreement with PRODES reference map (k=0,68, where k is de Kappa index). This result indicates that multipolarized L band SAR data have good potential to discriminate deforestation increments in the Amazon rainforest.
[Show abstract][Hide abstract] ABSTRACT: Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R(2) = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R(2) = 0.97), 2 (R(2) = 0.60), 3 (R(2) = 0.63) and 4 (R(2) = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.
Memórias do Instituto Oswaldo Cruz 07/2010; 105(4):524-31. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.
Memórias do Instituto Oswaldo Cruz 07/2010; 105(4):512-8. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R(2) = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.
Memórias do Instituto Oswaldo Cruz 07/2010; 105(4):532-6. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MG). We focus on socioeconomic and demographic features, basic sanitation features, the presence of accumulated water bodies, dense vegetation in the summer and winter seasons and related terrain characteristics. We draw on the decision tree approach to infection risk modelling and mapping. The model robustness was properly verified. The main variables that were selected by the procedure included the terrain's water accumulation capacity, temperature extremes and the Human Development Index. In addition, the model was used to generate two maps, one that included risk classification for the entire of MG and another that included classification errors. The resulting map was 62.9% accurate.
Memórias do Instituto Oswaldo Cruz 07/2010; 105(4):541-8. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The objective of this article is to evaluate the influence of the cross-talk and channel imbalance calibration on the estimation of the entropy and the alpha images. Few studies can be found in SAR literature concerning the influence of the polarimetric image calibration process on the target decomposition methods and their consequences on the characterization and discrimination of different ground targets. This influence is illustrated here by using a methodology based on an L-band fully polarimetric SAR data acquired by the SIPAM (Amazon Protection System) airborne R99-SAR over two areas of study, located in the Brazilian Amazon Forest and urban area regions.
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010
[Show abstract][Hide abstract] ABSTRACT: The use of phase information present in complex multi polarized images may increase the classification results. Thus, the coherence is one attribute that may be extracted from these images and used to distinguish some land cover classes. Therefore, its discriminatory capability for land use and land cover classification is analyzed. The analysis is based on the classification results of a region classifier, which needs a segmented image as one input. The influence of this kind of image input is also evaluated using of two segmentation algorithms, the SegSAR and the SPRING region growing. Two ALOS/PALSAR images acquired over Tapajós National Forest in the Brazilian Amazon were classified. The classifications were quantified by the overall accuracy, the kappa values and its variance. The classification improvement using the coherence information with intensity images was noticed for every image set.
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010
[Show abstract][Hide abstract] ABSTRACT: For scientific purposes the ALOS PALSAR sensor can, sporadically, provide full polarimetric SAR data (HH, HV and VV) In this paper the deterministic supervised classifier Support Vector Machines (SVM), have been studied to determine how much the use of full polarized (HH, VV and HV, no phase information) PALSAR data information can improve, or not, the overall classification accuracy in comparison with the standard products, which, for PALSAR instrument, is the HH (like JERS-1) or the dual polarization product HH-HV. The overall performance of this type of classifier is also of interest. The study area, Tapajós National Forest at the south of Santarém City, in the Brazilian Amazon, Pará State, has being object of intensive scientific observation and a set of seven classes were used to this assessment: primary forest, secondary forest, bare soil, two types of soy beans, pasture and degraded forest. The three polarimetric channels have also been filtered before the classification process by a 5 5 gamma filter, but the unfiltered channels were also put to test. Results showed that HH and HV filtered channels combination provided the best classification performance with 65%, but did not realize a very good separation between Primary and Secondary Forest.
[Show abstract][Hide abstract] ABSTRACT: Schistosomiasis mansoni is a disease with social and behavioral characteristics. Snails of the Biomphalaria species, the disease's intermediate host, use water as a vehicle to infect man, the disease main host. In Brazil, six million people are infected. From 1995 to 2005, more than a million positive cases were reported, 27% of them in the state of Minas Gerais. The objective of this paper is to estimate the prevalence risk of schistosomiasis, in terms of remote sensing, climate, socioeconomic, or neighborhood variables or a subset of them. We present two approaches for modeling and classifying the infection risk: a global and a regional one, both of them using the aforementioned variables. In the first approach, a unique regression model was generated and used to estimate the disease risk for the entire state. In the second approach, the state was divided in four regions, and a model was generated for each of them. The first model obtained 47.2% of overall accuracy (AC) and the second achieved 62.4%, which were considered unsatisfactory. To improve these results, the concept of imprecise classification, defined in terms of the standard deviation of estimates and several reliability levels, is used for the generation of two imprecise classification maps. The AC for the imprecise classification was 83.8% for the global model and 91.9% for the regional one, which were now considered acceptable. Particularly, regionalization has proven to be a good guideline to follow in future works involving geographical aspects and large data heterogeneity.
IEEE Transactions on Geoscience and Remote Sensing 01/2009; 47:3899-3908. · 3.47 Impact Factor