Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection

International audienceGraph-based methods are promising approaches for traditional and modern techniques in change detection (CD) applications. Nonetheless, some graph-based approaches omit the existence of useful priors that account for the structure of a scene, and the inter- and intra-relationships between the pixels are analyzed. To address this issue, in this article, we propose a framework for CD based on graph fusion and driven by graph signal smoothness representation. In addition to modifying the graph learning stage, in the proposed model, we apply a Gaussian mixture model for superpixel segmentation (GMMSP) as a downsampling module to reduce the computational cost required to learn the graph of the entire images. We carry out tests on 14 real cases of natural disasters, farming, and construction. The dataset contains homogeneous cases with multispectral (MS) and synthetic aperture radar (SAR) images, along with heterogeneous cases that include MS/SAR images. We compare our approach against probabilistic thresholding, unsupervised learning, deep learning, and graph-based methods. In terms of Cohen’s kappa coefficient, our proposed model based on graph signal smoothness representation outperformed state-of-the-art approaches in ten out of 14 datasets

Rationale, design, and baseline characteristics in Evaluation of LIXisenatide in Acute Coronary Syndrome, a long-term cardiovascular end point trial of lixisenatide versus placebo

BACKGROUND: Cardiovascular (CV) disease is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Furthermore, patients with T2DM and acute coronary syndrome (ACS) have a particularly high risk of CV events. The glucagon-like peptide 1 receptor agonist, lixisenatide, improves glycemia, but its effects on CV events have not been thoroughly evaluated. METHODS: ELIXA (www.clinicaltrials.gov no. NCT01147250) is a randomized, double-blind, placebo-controlled, parallel-group, multicenter study of lixisenatide in patients with T2DM and a recent ACS event. The primary aim is to evaluate the effects of lixisenatide on CV morbidity and mortality in a population at high CV risk. The primary efficacy end point is a composite of time to CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. Data are systematically collected for safety outcomes, including hypoglycemia, pancreatitis, and malignancy. RESULTS: Enrollment began in July 2010 and ended in August 2013; 6,068 patients from 49 countries were randomized. Of these, 69% are men and 75% are white; at baseline, the mean ± SD age was 60.3 ± 9.7 years, body mass index was 30.2 ± 5.7 kg/m(2), and duration of T2DM was 9.3 ± 8.2 years. The qualifying ACS was a myocardial infarction in 83% and unstable angina in 17%. The study will continue until the positive adjudication of the protocol-specified number of primary CV events. CONCLUSION: ELIXA will be the first trial to report the safety and efficacy of a glucagon-like peptide 1 receptor agonist in people with T2DM and high CV event risk