JOURNAL METRICS

Impact Factor (JCR) 2023: 1.2 ℹImpact Factor (JCR):

The JCR provides quantitative tools for ranking, evaluating, categorizing, and comparing journals. The impact factor is one of these; it is a measure of the frequency with which the “average article” in a journal has been cited in a particular year or period. The annual JCR impact factor is a ratio between citations and recent citable items published. Thus, the impact factor of a journal is calculated by dividing the number of current year citations to the source items published in that journal during the previous two years.

5-Year Impact Factor: 1.2 ℹ5-Year Impact Factor:

A 5-Year Impact Factor shows the long-term citation trend for a journal. This is calculated differently from the Journal Impact Factor, so it is not simply an average of the Impact Factors in the time period. The Impact Factor itself is based only on Web of Science Core Collection citation data from the last three years and thus reflects only recent impact. The Journal Impact Factor is the average number of times articles from the journal published in the past two years have been cited in the Journal Citation Reports year.

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Problème inverse en télédétection par imagerie à synthèse d’ouverture

Inverse problem in remote sensing by aperture synthesis imaging

E. Anterrieu, S. Gratton, B. Picard

Page 1-15

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Partition d’une séquence d’images temps-échelle pour la séparation d’ondes dans un profil sismique

Segmentation of time-frequency images for the separation of seismic waves

A. Roueff, J. Chanussot, J.I. Mars, M.Q. Nguyen

Page 17-36

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Champs de Markov flous pour images multispectrales

Fuzzy Markov random fields for multispectral images

F. Salzenstein, Ch. Collet, M. Petremand

Page 37-53

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Détection de défauts temps réel sur des objets à géométrie complexe: étude par svm, boosting et hyperrectangles

Real-time flaw detection on complex part : study of svm, boosting and hyperrectangle based method

S. Bouillant, J. Mitéran, M. Paindavoine, J. Matas

Page 55-69

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Stratégies de rejet en classification supervisée: une synthèse par opérateurs de De Morgan

Reject strategies for supervised classification with De Morgan operators : a review

C. Frélicot, L. Mascarilla

Page 71-87

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Réduction du nombre de niveaux de lissage dans une structure de filtre LUM FTC

Reduction of smoothing levels in LUM FTC filter structure

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