Multisource and multitemporal data fusion in remote sensing:A comprehensive review of the state of the art

The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several

Meta-analysis of type 2 Diabetes in African Americans Consortium

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)<P<5 × 10(-8), odds ratio (OR)  = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2 × 10(-23) < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.Peer reviewe

Classification based marker selection for watershed transform of hyperspectral images

International audienc

A Novel Hierarchical Clustering Technique Based on Splitting and Merging

Amongst the multiple benefits and uses of remote sensing, one of the most important applications is to solve the problem of land-cover mapping. In this paper, unsupervised techniques are considered for land-cover mapping using multispectral satellite images. In unsupervised techniques, automatic generation of the number of clusters for huge databases has not been exploited to its full potential. To overcome that, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed here. In the proposed method, a splitting method is initially used to search for the best possible number of clusters with a non-parametric estimation technique, i.e., mean shift clustering (MSC). For the obtained clusters, a merging method is used to group the data points based on a parametric method (k-means clustering algorithm). The performance of the proposed hierarchical clustering algorithm is compared with three previously proposed unsupervised algorithms, i.e., (1) parametric k-means clustering; (2) hybrid MSC and k-means clustering; (3) hybrid algorithm for cluster establishment (ACE) and k-means clustering. Two typical multispectral satellite images – a Landsat 7 thematic mapper image and a QuickBird image are used to demonstrate the performance of the proposed hierarchical clustering algorithm. A performance comparison of this proposed algorithm with the previously proposed algorithms is presented. From the obtained results, it is concluded that the proposed hierarchical clustering algorithm is both more accurate and more robust than the other compared algorithms