Interesting spatiotemporal rules discovery: application to remotely sensed image databases

International audiencePurpose Knowledge discovery in databases aims to discover useful and significant information from multiple databases. However, in the remote sensing field, the large size of discovered information makes it hard to manually look for interesting information quickly and easily. The purpose of this paper is to automate the process of identifying interesting spatiotemporal knowledge (expressed as rules). Design/methodology/approach The proposed approach is based on case-based reasoning (CBR) process. CBR allows the recognition of useful and interesting rules by simulating a human reasoning process, and combining objective and subjective interestingness measures. It takes advantage of statistics' power from objective criteria and the reliability of subjective criteria. This helps improve the discovery of interesting rules by taking into consideration the different properties of interestingness measures. Findings The proposed approach combines several interestingness measures with complementary properties to improve the detection of the interesting rules. Based on a CBR process, it, also, offers three main advantages to users in a remote sensing field: automatism, integration of the users' expectations and combination of several interestingness measures while taking into account the reliability of each one. The performance of the proposed approach is evaluated and compared to other approaches using several real-world datasets. Originality/value This study reports a valuable decision support tool for engineers, environmental authority and personnel who want to identify relevant discovered rules. The resulting rules are useful for many fields such as: disaster prevention and monitoring, growth volume and crops on farm or grassland, planting status of agricultural products, and tree distribution of forests

Towards a multi-approach system for uncertain spatio-temporal knowledge discovery in satellite imagery

International audienceExploiting images coming from different sensors is an important challenge in the remote sensing field. Integration of new knowledge is crucial to help the user interpret satellite images and track their spatio-temporal changes over time. Thus, we propose a new approach to process multi-date satellite images. Our approach combines knowledge discovery from satellite image databases, and fusion methods in order to find out new and relevant knowledge useful to create decision making and prevision models. The choice of the proposed architecture is motivated by two reasons. First, we need to process imperfection related to the knowledge discovery and interpretation processes. Second, we should integrate new, valid, potentially useful and ultimately understandable knowledge hidden in databases. Our work is based on three concepts (multi-agent systems, case based reasoning and rule based reasoning) and is validated through the use of two optical satellite images coming from Landsat 7 representing the region of Matmata (South of Tunisia

Spatio-temporal modeling for knowledge discovery in satellite image databases

International audienceKnowledge discovery from satellite images in spatio-temporal context remains one of the major challenges in the remote sensing field. It is, always, difficult for a user to manually extract useful information especially when processing a large collection of satellite images. Thus, we need to use automatic knowledge discovery in order to develop intelligent image interpretation systems. In this paper, we present a high-level approach for modeling spatio-temporal knowledge from satellite images. We also propose to use a multi-approach segmentation involving several segmentation methods which help improving images modeling and interpretation. The experiments, made on LANDSAT scenes, show that our approach outperforms classical methods in image segmentation and are able to predict spatio-temporal changes of satellite images

A data mining based approach to predict spatiotemporal changes in satellite images

International audienceThe interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited.This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone

Phylogenetic Analysis and Epidemic History of Hepatitis C Virus Genotype 2 in Tunisia, North Africa.

HCV genotype 2 (HCV-2) has a worldwide distribution with prevalence rates that vary from country to country. High genetic diversity and long-term endemicity were suggested in West African countries. A global dispersal of HCV-2 would have occurred during the 20th century, especially in European countries. In Tunisia, genotype 2 was the second prevalent genotype after genotype 1 and most isolates belong to subtypes 2c and 2k. In this study, phylogenetic analyses based on the NS5B genomic sequences of 113 Tunisian HCV isolates from subtypes 2c and 2k were carried out. A Bayesian coalescent-based framework was used to estimate the origin and the spread of these subtypes circulating in Tunisia. Phylogenetic analyses of HCV-2c sequences suggest the absence of country-specific or time-specific variants. In contrast, the phylogenetic grouping of HCV-2k sequences shows the existence of two major genetic clusters that may represent two distinct circulating variants. Coalescent analysis indicated a most recent common ancestor (tMRCA) of Tunisian HCV-2c around 1886 (1869-1902) before the introduction of HCV-2k in 1901 (1867-1931). Our findings suggest that the introduction of HCV-2c in Tunisia is possibly a result of population movements between Tunisia and European population following the French colonization

Prevalence of hepatitis C virus (HCV) variants resistant to NS5A inhibitors in naïve patients infected with HCV genotype 1 in Tunisia

International audienceBackground: Hepatitis C virus (HCV) non-structural protein 5A (NS5A) inhibitors have been recently developed to inhibit NS5A activities and have been approved for the treatment of HCV infection. However the drawback of these direct acting antivirals (DAAs) is the emergence of resistance mutations. The prevalence of such mutations conferring resistance to HCV-NS5A inhibitors before treatment has not been investigated so far in the Tunisian population. The aim of this study was to detect HCV variants resistant to HCV-NS5A inhibitors in hepatitis C patients infected with HCV genotype 1 before any treatment with NS5A inhibitors. Methods: Amplification and direct sequencing of the HCV NS5A region was carried out on 112 samples from 149 untreated patients. Results: In genotype 1a strains, amino acid substitutions conferring resistance to NS5A inhibitors (M28V) were detected in 1/7 (14.2 %) HCV NS5A sequences analyzed. In genotype 1b, resistance mutations in the NS5A region (R30Q; L31M; P58S and Y93H) were observed in 17/105 (16.2 %) HCV NS5A sequences analyzed. R30Q and Y93H (n = 6; 5.7 %) predominated over P58S (n = 4; 3.8 %) and L31M (n = 3; 2.8 %). Conclusions: Mutations conferring resistance to HCV NS5A inhibitors are frequent in treatment-naive Tunisian patients infected with HCV genotype 1b. Their influence in the context of DAA therapies has not been fully investigated and should be taken into consideration

Molecular epidemiology of hepatitis B and Delta virus strains that spread in the Mediterranean North East Coast of Tunisia

International audienceBackground: Tunisia is classified as an area of middle endemic for hepatitis B virus (HBV) infection, however little is known about hepatitis Delta virus (HDV) infection. Objectives: This study aimed to address the prevalence of HDV infection, to identify possible risks factors, and to analyze the genetic diversity of HDV strains that are spreading in Tunisia. Study design: A retrospective large-scale study including 1615 HBsAg positive patients, native of the North East coast of Tunisia, recruited from Gastroenterology departments, was conducted. Demographic, epidemiological, ethnical, clinical and biological data were recorded. HBV and HDV serological analyses and DNA and RNA viral load quantification were performed. Genotyping of HBV and HDV strains was performed using nucleotide sequencing followed by phylogenetic analyses. Results: The study population included 819(50.7%) men and 796(49.3%) women; aged 12-90 years (mean age 41 + 13 years). A very low prevalence of HDV infection, 2% was observed. No risk factor, except a history of hospitalization for surgery was found. All HDV strains belonged to genotype 1, with a wide distribution within the HDV-1 group. They all share the African amino acid marker, a serine at position 202 of the large Delta protein. HBV genotypes were distributed as follows: HBV/D1 (56.8%), HBV/D7 (40.9%), and HBV/A2 (2.3%). Conclusion: Tunisia is a low endemic region for HDV infection, due to an efficient policy of HBV infection control. HDV-1 is the sole genotype found, with a high diversity within this group. Further studies are ongoing in order to better characterize and manage the HBV/HDV-infected patients according to the genetic variability of the viral strains. (C) 2015 Elsevier B.V. All rights reserved