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CSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6059 - 6070 Titre : CSVM architectures for pixel-wise object detection in high-resolution remote sensing images Type de document : Article/Communication Auteurs : Youyou Li, Auteur ; Farid Melgani, Auteur ; Binbin He, Auteur Année de publication : 2020 Article en page(s) : pp 6059 - 6070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection d'objet [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image captée par drone [Termes IGN] processeur graphique Résumé : (auteur) Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. Therefore, GPU-based methods are less attractive in practical applications. In this article, we propose a CPU-based method that is based on convolutional support vector machines (CSVMs) to address the object detection challenge in VHR images. Experiments are conducted on three VHR and two unmanned aerial vehicle (UAV) data sets with very limited training data. Results show that the proposed CSVM achieves competitive performance compared to U-Net which is an efficient CNN-based model designed for small training data sets. Numéro de notice : A2020-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2972289 Date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2972289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95705 [article]

Wave period and coastal bathymetry using wave propagation on optical images / Céline Danilo in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6307 - 6319 Titre : Wave period and coastal bathymetry using wave propagation on optical images Type de document : Article/Communication Auteurs : Céline Danilo, Auteur ; Farid Melgani, Auteur Année de publication : 2016 Article en page(s) : pp 6307 - 6319 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bathymétrie [Termes IGN] fréquence [Termes IGN] Hawaii (Etats-Unis) [Termes IGN] image Landsat-8 [Termes IGN] image optique [Termes IGN] lever bathymétrique [Termes IGN] littoral [Termes IGN] rayonnement électromagnétique [Termes IGN] vague Résumé : (Auteur) We propose a method based on combining wave tracing and linear wave theory for the estimation of wave period and bathymetry in coastal areas from satellite images. The method depends on several parameters for which we provide ranges of variations adapted to the instrument. Experimental results are conducted on several sites located around the Hawaiian island of Oahu, using 13 Landsat-8 images. Results show that wave period estimations are compatible with the wave buoy measurements in all cases. In addition, bathymetry estimation results show a standard deviation of less than 15% of the observed depth out of the surf zone until 20 m for sites with a direct exposure to the swell and with an absence of clouds. The proposed method, which does not rely on ancillary data, represents a promising tool for bathymetry estimation using satellite images in which waves are present. Numéro de notice : A2016-912 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2579266 En ligne : https://doi.org/10.1109/TGRS.2016.2579266 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83134 [article]

Missing-area reconstruction in multispectral images under a compressive sensing perspective / Luca Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3998 - 4008 Titre : Missing-area reconstruction in multispectral images under a compressive sensing perspective Type de document : Article/Communication Auteurs : Luca Lorenzi, Auteur ; Farid Melgani, Auteur ; Grégoire Mercier, Auteur Année de publication : 2013 Article en page(s) : pp 3998 - 4008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par algorithme génétique [Termes IGN] équation linéaire [Termes IGN] image Formosat/COSMIC [Termes IGN] image SPOT 5 [Termes IGN] nébulosité [Termes IGN] nuage [Termes IGN] régression Résumé : (Auteur) The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. Numéro de notice : A2013-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227329 En ligne : https://doi.org/10.1109/TGRS.2012.2227329 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32510 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013071A	RAB	Revue	Centre de documentation	En réserve L003	Disponible