The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Feng Ling - One of the best experts on this subject based on the ideXlab platform.
-
spatial Temporal super resolution land cover mapping with a local spatial Temporal Dependence model
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Feng Ling, Giles M Foody, Yihang Zhang, Lihui Wang, Lingfei ShiAbstract:The mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial–Temporal SRM (STSRM) extends the basic SRM to include a Temporal dimension by using a finer-spatial resolution land cover map that pre- or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time-series data. In addition, the STSRM methods define the Temporal Dependence globally, and neglect the spatial variation of land cover Temporal Dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local Temporal Dependence model, in which the Temporal Dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods.
-
super resolution land cover mapping with spatial Temporal Dependence by integrating a former fine resolution map
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Feng Ling, Fei XiaoAbstract:Super-resolution mapping (SRM) is a technique to predict spatial locations of land cover classes at the subpixel scale within coarse resolution remotely sensed image pixels. Due to the lack of information about the spatial pattern of land covers, uncertainty always exists in resultant fine-resolution land cover maps. In the present work, by integrating a former fine-resolution land cover map, the spatial Dependence used in existing SRM algorithms is extended into a novel spatial–Temporal Dependence used in the SRM algorithm (SRM_STD). The spatial–Temporal Dependence consists of the spatial Dependences of former fine-resolution land cover map, the spatial Dependences of latter coarse resolution fraction images, and the corresponding Dependence between former and latter land cover maps. By considering the spatial–Temporal Dependences of subpixels, SRM_STD can inherit valuable land cover information from the former fine-resolution land cover map, and reduce the uncertainty of SRM to a large extent. The performance of the proposed SRM_STD algorithm is assessed using a subset of the National Land Cover Database datasets and land cover maps produced by Landsat imagery in an area of rapid urban expansion. The results of two experiments show that the former Dependence has little influence on the result, whereas the corresponding Dependence plays a crucial role on the result. With a large weight of corresponding Dependence, the proposed SRM_STD algorithm can produce fine-resolution land cover maps with higher accuracy than those of hard classification and the pixel swapping algorithm.
Lingfei Shi - One of the best experts on this subject based on the ideXlab platform.
-
spatial Temporal super resolution land cover mapping with a local spatial Temporal Dependence model
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Feng Ling, Giles M Foody, Yihang Zhang, Lihui Wang, Lingfei ShiAbstract:The mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial–Temporal SRM (STSRM) extends the basic SRM to include a Temporal dimension by using a finer-spatial resolution land cover map that pre- or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time-series data. In addition, the STSRM methods define the Temporal Dependence globally, and neglect the spatial variation of land cover Temporal Dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local Temporal Dependence model, in which the Temporal Dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods.
David B Dunson - One of the best experts on this subject based on the ideXlab platform.
-
bayesian modeling of Temporal Dependence in large sparse contingency tables
Journal of the American Statistical Association, 2013Co-Authors: Tsuyoshi Kunihama, David B DunsonAbstract:It is of interest in many applications to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party, and preference for particular policies. At each time point, a sample of individuals provides responses to a set of questions, with different individuals sampled at each time. In such settings, there tend to be an abundance of missing data and the variables being measured may change over time. At each time point, we obtained a large sparse contingency table, with the number of cells often much larger than the number of individuals being surveyed. To borrow information across time in modeling large sparse contingency tables, we propose a Bayesian autoregressive tensor factorization approach. The proposed model relies on a probabilistic Parafac factorization of the joint pmf characterizing the categorical data distribution at each time point, with autocorrelation included across times. We develop efficient computational methods th...
-
bayesian modeling of Temporal Dependence in large sparse contingency tables
arXiv: Methodology, 2012Co-Authors: Tsuyoshi Kunihama, David B DunsonAbstract:In many applications, it is of interest to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party and preference for particular policies. At each time point, a sample of individuals provide responses to a set of questions, with different individuals sampled at each time. In such settings, there tends to be abundant missing data and the variables being measured may change over time. At each time point, one obtains a large sparse contingency table, with the number of cells often much larger than the number of individuals being surveyed. To borrow information across time in modeling large sparse contingency tables, we propose a Bayesian autoregressive tensor factorization approach. The proposed model relies on a probabilistic Parafac factorization of the joint pmf characterizing the categorical data distribution at each time point, with autocorrelation included across times. Efficient computational methods are developed relying on MCMC. The methods are evaluated through simulation examples and applied to social survey data.
Fei Xiao - One of the best experts on this subject based on the ideXlab platform.
-
super resolution land cover mapping with spatial Temporal Dependence by integrating a former fine resolution map
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Feng Ling, Fei XiaoAbstract:Super-resolution mapping (SRM) is a technique to predict spatial locations of land cover classes at the subpixel scale within coarse resolution remotely sensed image pixels. Due to the lack of information about the spatial pattern of land covers, uncertainty always exists in resultant fine-resolution land cover maps. In the present work, by integrating a former fine-resolution land cover map, the spatial Dependence used in existing SRM algorithms is extended into a novel spatial–Temporal Dependence used in the SRM algorithm (SRM_STD). The spatial–Temporal Dependence consists of the spatial Dependences of former fine-resolution land cover map, the spatial Dependences of latter coarse resolution fraction images, and the corresponding Dependence between former and latter land cover maps. By considering the spatial–Temporal Dependences of subpixels, SRM_STD can inherit valuable land cover information from the former fine-resolution land cover map, and reduce the uncertainty of SRM to a large extent. The performance of the proposed SRM_STD algorithm is assessed using a subset of the National Land Cover Database datasets and land cover maps produced by Landsat imagery in an area of rapid urban expansion. The results of two experiments show that the former Dependence has little influence on the result, whereas the corresponding Dependence plays a crucial role on the result. With a large weight of corresponding Dependence, the proposed SRM_STD algorithm can produce fine-resolution land cover maps with higher accuracy than those of hard classification and the pixel swapping algorithm.
Amy Kuceyeski - One of the best experts on this subject based on the ideXlab platform.
-
sex classification using long range Temporal Dependence of resting state functional mri time series
Human Brain Mapping, 2020Co-Authors: Elvisha Dhamala, Keith Jamison, Mert R Sabuncu, Amy KuceyeskiAbstract:A thorough understanding of sex differences that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit phenotypic differences between males and females. Here we evaluate sex differences in regional Temporal Dependence of resting-state brain activity in 195 adult male-female pairs strictly matched for total grey matter volume from the Human Connectome Project. We find that males have more persistent Temporal Dependence in regions within Temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional Temporal Dependence measures achieve sex classification accuracies up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, and frontal and occipital cortices. Secondarily, we show that even after strict matching of total gray matter volume, significant volumetric sex differences persist; males have larger absolute cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger absolute cingulates, precunei, and frontal and parietal cortices. Sex classification based on regional volume achieves accuracies up to 85%, highlighting the importance of strict volume-matching when studying brain-based sex differences. Differential patterns in regional Temporal Dependence between the sexes identifies a potential neurobiological substrate or environmental effect underlying sex differences in functional brain activation patterns.
-
sex classification using long range Temporal Dependence of resting state functional mri time series
bioRxiv, 2019Co-Authors: Elvisha Dhamala, Keith Jamison, Mert R Sabuncu, Amy KuceyeskiAbstract:ABSTRACT DNA regulatory motifs reflect the direct transcriptional interactions between regulators and their target genes and contain important information regarding transcriptional networks. In silico motif detection strategies search for DNA patterns that are present more frequently in a set of related sequences than in a set of unrelated sequences. Related sequences could be genes that are coexpressed and are therefore expected to share similar conserved regulatory motifs. We identified coexpressed genes by carrying out microarray-based transcript profiling of Salmonella enterica serovar Typhimurium in response to the spent culture supernatant of the probiotic strain Lactobacillus rhamnosus GG. Probiotics are live microorganisms which, when administered in adequate amounts, confer a health benefit on the host. They are known to antagonize intestinal pathogens in vivo, including salmonellae. S. enterica serovar Typhimurium causes human gastroenteritis. Infection is initiated by entry of salmonellae into intestinal epithelial cells. The expression of invasion genes is tightly regulated by environmental conditions, as well as by many bacterial factors including the key regulator HilA. One mechanism by which probiotics may antagonize intestinal pathogens is by influencing invasion gene expression. Our microarray experiment yielded a cluster of coexpressed Salmonella genes that are predicted to be down-regulated by spent culture supernatant. This cluster was enriched for genes known to be HilA dependent. In silico motif detection revealed a motif that overlaps the previously described HilA box in the promoter region of three of these genes, spi4_H, sicA, and hilA. Site-directed mutagenesis, β-galactosidase reporter assays, and gel mobility shift experiments indicated that sicA expression requires HilA and that hilA is negatively autoregulated.
