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Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])  

Public [article]  inGeocarto international > vol 35 n° 5 [01/04/2020] . - pp 535 - 558 Titre : Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys Type de document : Article/Communication Auteurs : Ashutosh Tiwari, Auteur ; Avadh Bihari Narayan, Auteur ; Ramji Dwivedi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 535 - 558 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arpentage [Termes IGN] corrélation croisée maximale [Termes IGN] covariance [Termes IGN] données GNSS [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] effondrement de terrain [Termes IGN] escarpement [Termes IGN] image Sentinel-SAR [Termes IGN] Inde [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] modèle numérique de surface [Termes IGN] précipitation [Termes IGN] surveillance géologique [Termes IGN] tachéomètre électronique robotisé Résumé : (auteur) A robust geodetic framework comprising Terrestrial Laser Scanner (TLS), Global Navigation Satellite Systems (GNSS), Robotic Total Station (RTS) and Multi-temporal InSAR (MT-InSAR) was employed first in India to investigate a landslide-prone Sirobagarh region, Uttarakhand, at different spatial extents, and to evaluate the relationship amongst the displacement estimates obtained from the applied surveying techniques. TLS derived digital elevation models indicated displacements >5 m on the landslide upper scarp. GNSS- and RTS-based observations showed horizontal movements towards the Alaknanda river in the landslide slope direction (maximum values: 0.1305 and 0.045 m, respectively), and downward vertical motion (largest subsidence magnitude: −2.1315 and −0.030 m, respectively). MT-InSAR processing of Sentinel-1a images identified 21071 measurement pixels, highlighting subsidence around the landslide (mean velocity range: −0.110 to 0.008 m/year). Analysis of displacement vectors using vector equality, cross-covariance, cross-correlation and principal component analysis reveals that GNSS vertical displacement estimates were partially correlated with MT-InSAR measurements (correlated for epoch difference 2–3), whereas there was good cross-correlation between MT-InSAR and LiDAR observations throughout. The displacement estimates and their analyses evident unstable movement of the landslide scarp occurring due to debris flow and rainfall, and a relatively moderate subsidence activity in the surrounding areas lying in the landslide zone. Numéro de notice : A2020-144 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1524516 Date de publication en ligne : 23/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1524516 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94770 [article]

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059-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Very high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)  

Public Titre : Very high resolution land cover mapping of urban areas at global scale with convolutional neural network Type de document : Article/Communication Auteurs : Thomas Tilak , Auteur ; Arnaud Braun , Auteur ; David Chandler , Auteur ; Nicolas David , Auteur ; Sylvain Galopin , Auteur ; Amélie Lombard, Auteur ; Camille Parisel , Auteur ; Camille Parisel , Auteur ; Matthieu Porte , Auteur ; Marjorie Robert, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Autre Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B3 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 3, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 3 Importance : 8 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] BD Alti [Termes IGN] carte d'occupation du sol [Termes IGN] chaîne de production [Termes IGN] chaîne de traitement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corrélation croisée maximale [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] Gironde (33) [Termes IGN] image à très haute résolution [Termes IGN] image aérienne [Termes IGN] image multibande [Termes IGN] modèle numérique de surface [Termes IGN] segmentation sémantique [Termes IGN] vectorisation [Termes IGN] zone d'intérêt [Termes IGN] zone urbaine Résumé : (auteur) This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model. We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class. A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions. The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization. Numéro de notice : C2020-038 Affiliation des auteurs : IGN+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B3-2020-201-2020 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-201-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95079

A class of cloud detection algorithms based on a MAP-MRF approach in space and time / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5100 - 5115 Titre : A class of cloud detection algorithms based on a MAP-MRF approach in space and time Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Paolo Adesso, Auteur ; Maurizio Longo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5100 - 5115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] classification [Termes IGN] corrélation croisée maximale [Termes IGN] densité de probabilité [Termes IGN] détection des nuages Résumé : (Auteur) A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms. Numéro de notice : A2014-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2286834 En ligne : https://doi.org/10.1109/TGRS.2013.2286834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73972 [article]

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Computing coastal ocean surface curreants from infrared and ocean color satellite imagery / R.I. Crocker in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 2 (February 2007) . - pp 435 - 447 Titre : Computing coastal ocean surface curreants from infrared and ocean color satellite imagery Type de document : Article/Communication Auteurs : R.I. Crocker, Auteur Année de publication : 2007 Article en page(s) : pp 435 - 447 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] Californie (Etats-Unis) [Termes IGN] corrélation croisée maximale [Termes IGN] couleur de l'océan [Termes IGN] courant marin [Termes IGN] image Aqua-MODIS [Termes IGN] image NOAA-AVHRR [Termes IGN] image Seawifs [Termes IGN] littoral [Termes IGN] rayonnement infrarouge thermique [Termes IGN] spectroradiométrie [Termes IGN] température de surface [Termes IGN] thermographie Résumé : (Auteur) Many previous studies have demonstrated the viability of estimating advective ocean surface currents from sequential infrared satellite imagery using the maximum cross-correlation (MCC) technique when applied to 1.1-km-resolution Advanced Very High Resolution Radiometer (AVHRR) thermal infrared imagery. Applied only to infrared imagery, cloud cover and undesirable viewing conditions (gaps in satellite data and edge-of-scan distortions) limit the spatial and temporal coverage of the resulting velocity fields. In addition, MCC currents are limited to those represented by the displacements of thermal surface patterns, and hence, isothermal flow is not detected by the MCC method. The possibility of supplementing MCC currents derived from thermal AVHRR imagery was examined, with currents calculated from 1.1-km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color imagery, which often have spatial patterns complementary to the thermal infrared patterns. Statistical comparisons are carried out between yearlong collections of thermal and ocean color derived MCC velocities for the central California Current. It is found that the image surface patterns and resulting MCC velocities complement one another to reduce the effects of poor viewing conditions and isothermal flow. The two velocity products are found to agree quite well with a mean correlation of 0.74, a mean rms difference of 7.4 cm/s, and a mean bias less than 2 cm/s which is considerably smaller than the established absolute error of the MCC method. Merging the thermal and ocean color MCC velocity fields increases the spatial coverage by approximately 25% for this specific case study. Copyright IEEE Numéro de notice : A2007-078 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.883461 En ligne : https://doi.org/10.1109/TGRS.2006.883461 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28443 [article]

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065-07021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-07022	RAB	Revue	Centre de documentation	En réserve L003	Disponible