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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Snow cover change assessment in the upper Bhagirathi basin using an enhanced cloud removal algorithm / Mritunjay Kumar Singh in Geocarto international, vol 36 n° 20 ([01/12/2021])
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[article]
Titre : Snow cover change assessment in the upper Bhagirathi basin using an enhanced cloud removal algorithm Type de document : Article/Communication Auteurs : Mritunjay Kumar Singh, Auteur ; Renoj J. Thayyen, Auteur ; Sanjay K. Jain, Auteur Année de publication : 2021 Article en page(s) : pp 2279 - 2302 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] bassin hydrographique
[Termes IGN] bilan de masse
[Termes IGN] changement climatique
[Termes IGN] eau de fonte
[Termes IGN] filtrage spatiotemporel
[Termes IGN] glacier
[Termes IGN] Himalaya
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] Inde
[Termes IGN] manteau neigeux
[Termes IGN] MNS ASTER
[Termes IGN] nébulosité
[Termes IGN] nuage
[Termes IGN] variation saisonnièreRésumé : (auteur) This research paper proposes a new five-step protocol to enhance the result of existing cloud removal algorithms using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products (SCPs). The study has been carried out for the upper Bhagirathi basin (up to Maneri Hydropower Project) located in the Western Himalaya. Gafurov and Bárdossy test employed to validate the performance of the proposed method, followed by comparing with the field observed snow cover duration (SCD) data. The result shows that the mean overall accuracy of the proposed method for cloud removal is about ∼95%. However, the cloud removal method by Gafurov and Bardossy also achieved similar mean overall accuracy but with the higher variability within the individual images as compared with the variability within the results obtained by the proposed method. SCD computed from cloud removed SCPs matched significantly with the field observed SCD for a point location, supporting the accuracy achieved by the cloud removal method. This study also examines the spatiotemporal variability of the snow cover in the study area during the past 18 years (2000–2018). During the observation period, no specific trend was observed for annual maximum snow cover, while yearly minimum snow cover in the basin showed an increasing trend since 2010. Seasonally, December and June month witnessed significant changes. December experienced a declining trend in snow cover between 3000–6000 m a.s.l. covering 88% of the basin area, whereas, June showed an increasing trend between 4500 to 6000 m (a.s.l.). This elevation range covers 61% of the basin area, including core 86% of the glacier area within the basin. September and October experienced the highest inter-annual snow cover variability. Maximum snow cover month of February and minimum snow cover month of August experienced the least variability. The present study suggests significant elevation-dependent increasing as well as the decreasing trend in the snow cover with seasonal contrast, which may affect the glaciers as well as the hydrological behavior of the basin. Numéro de notice : A2021-832 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1704069 Date de publication en ligne : 19/12/2021 En ligne : https://doi.org/10.1080/10106049.2019.1704069 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99005
in Geocarto international > vol 36 n° 20 [01/12/2021] . - pp 2279 - 2302[article]Utility-pole detection based on interwoven column generation from terrestrial mobile Laser scanner data / Siamak Talebi Nahr in Photogrammetric record, Vol 36 n° 176 (December 2021)
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Titre : Utility-pole detection based on interwoven column generation from terrestrial mobile Laser scanner data Type de document : Article/Communication Auteurs : Siamak Talebi Nahr, Auteur ; Mohammad Saadatseresht, Auteur Année de publication : 2021 Article en page(s) : pp 402 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] équipement collectif
[Termes IGN] exactitude des données
[Termes IGN] exhaustivité des données
[Termes IGN] lidar mobile
[Termes IGN] mur
[Termes IGN] objet géographique complexe
[Termes IGN] objet géographique urbain
[Termes IGN] partitionnement par bloc
[Termes IGN] qualité des données
[Termes IGN] réseau électrique
[Termes IGN] scène urbaineRésumé : (Auteur) Mobile lidar scanning is one of the recent technologies that is used to map street scenes rapidly. Among street objects, utility-poles are more critical to energy companies to monitor regularly through time. This paper presents a novel approach to detect utility-poles from mobile lidar data in complex city scenes. After removing ground points, the scene is gridded into blocks based on a shared-partitioning algorithm. Next, an interwoven column generation algorithm is used to create columns. Finally, each of these columns is considered to be a utility-pole or not. The proposed algorithm is tested on two test areas. The algorithm achieved Completeness, Correctness and Quality of 92.8%, 97.5% and 90.6% in Area 1, and 92.8%, 92.2% and 86.1% in Area 2. The total number of utility-poles in both areas was 265. The algorithm shows promising results in utility-pole detection in complex city scenes with attached walls. Numéro de notice : A2021-916 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12394 Date de publication en ligne : 10/12/2021 En ligne : https://doi.org/10.1111/phor.12394 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99331
in Photogrammetric record > Vol 36 n° 176 (December 2021) . - pp 402 - 424[article]Feature matching for multi-epoch historical aerial images: A new pipeline feature detection pipeline in open-source MicMac / Lulin Zhang in Blog de la RFPT, sans n° ([17/11/2021])
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Titre : Feature matching for multi-epoch historical aerial images: A new pipeline feature detection pipeline in open-source MicMac Type de document : Article/Communication Auteurs : Lulin Zhang , Auteur ; Ewelina Rupnik
, Auteur ; Marc Pierrot-Deseilligny
, Auteur
Année de publication : 2021 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de formes
[Termes IGN] chaîne de traitement
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] détection de changement
[Termes IGN] données multitemporelles
[Termes IGN] image aérienne
[Termes IGN] image ancienne
[Termes IGN] image RVB
[Termes IGN] MicMac
[Termes IGN] modèle numérique de surfaceRésumé : (Auteur) [Introduction] ... We propose a fully automatic approach to computing robust inter-epoch feature correspondences. Our method consists of two steps: a rough co-registration by finding feature correspondences between DSMs (Digital Surface Model) derived within single epochs, and a precise feature matching on original RGB images. Our main contributions include:
- Rough-to-precise matching strategy that helps to drastically reduce ambiguity. In particular, we use the depth information to roughly co-register our epochs. The 3D landscape is globally stable over time and provides sufficient correspondences through time. Once co-registered, we levarage the 3D a priori to narrow down the search space in precise matching.
- Upscaling of the learning based feature matching algorithms to high resolution imagery. To do that, we introduced an image tiling scheme.
In the following we present the methodology and some experiments. If you are interested in using our method, please refer to the source code of MicMac Github 4, as well as 2 jupyter tutorials 5 6. We also provide an introduction video 7, slides 8 and project website 9. The datasets used in our publication 3 will be soon published in an open-access repository.Numéro de notice : A2021-840 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 17/11/2021 En ligne : https://rfpt-sfpt.github.io/blog/feature%20matching/historical%20images/multi-ep [...] Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99042
in Blog de la RFPT > sans n° [17/11/2021][article]Crop rotation modeling for deep learning-based parcel classification from satellite time series / Félix Quinton in Remote sensing, vol 13 n° 22 (November-2 2021)
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Titre : Crop rotation modeling for deep learning-based parcel classification from satellite time series Type de document : Article/Communication Auteurs : Félix Quinton , Auteur ; Loïc Landrieu
, Auteur
Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° 4599 Note générale : bibliographie
This research was funded by the French Payment Agency ASP.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] carte agricole
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] image Sentinel-MSI
[Termes IGN] parcelle agricole
[Termes IGN] rotation de culture
[Termes IGN] série temporelleRésumé : (auteur) While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels. Numéro de notice : A2021-934 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13224599 Date de publication en ligne : 16/11/2021 En ligne : https://doi.org/10.3390/rs13224599 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99539
in Remote sensing > vol 13 n° 22 (November-2 2021) . - n° 4599[article]Spatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)
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Titre : Spatial variability of suspended sediments in San Francisco Bay, California Type de document : Article/Communication Auteurs : Niky C. Taylor, Auteur ; Raphael M. Kudela, Auteur Année de publication : 2021 Article en page(s) : n° 4625 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] baie
[Termes IGN] échantillonnage
[Termes IGN] estuaire
[Termes IGN] image Sentinel-MSI
[Termes IGN] pas d'échantillonnage au sol
[Termes IGN] qualité des eaux
[Termes IGN] réflectance
[Termes IGN] San Francisco
[Termes IGN] sédiment
[Termes IGN] spectroradiométrie
[Termes IGN] surface de l'eau
[Termes IGN] surveillance du littoral
[Termes IGN] turbidité des eaux
[Termes IGN] variabilitéRésumé : (auteur) Understanding spatial variability of water quality in estuary systems is important for making monitoring decisions and designing sampling strategies. In San Francisco Bay, the largest estuary system on the west coast of North America, tracking the concentration of suspended materials in water is largely limited to point measurements with the assumption that each point is representative of its surrounding area. Strategies using remote sensing can expand monitoring efforts and provide a more complete view of spatial patterns and variability. In this study, we (1) quantify spatial variability in suspended particulate matter (SPM) concentrations at different spatial scales to contextualize current in-water point sampling and (2) demonstrate the potential of satellite and shipboard remote sensing to supplement current monitoring methods in San Francisco Bay. We collected radiometric data from the bow of a research vessel on three dates in 2019 corresponding to satellite overpasses by Sentinel-2, and used established algorithms to retrieve SPM concentrations. These more spatially comprehensive data identified features that are not picked up by current point sampling. This prompted us to examine how much variability exists at spatial scales between 20 m and 10 km in San Francisco Bay using 10 m resolution Sentinel-2 imagery. We found 23–80% variability in SPM at the 5 km scale (the scale at which point sampling occurs), demonstrating the risk in assuming limited point sampling is representative of a 5 km area. In addition, current monitoring takes place along a transect within the Bay’s main shipping channel, which we show underestimates the spatial variance of the full bay. Our results suggest that spatial structure and spatial variability in the Bay change seasonally based on freshwater inflow to the Bay, tidal state, and wind speed. We recommend monitoring programs take this into account when designing sampling strategies, and that end-users account for the inherent spatial uncertainty associated with the resolution at which data are collected. This analysis also highlights the applicability of remotely sensed data to augment traditional sampling strategies. In sum, this study presents ways to supplement water quality monitoring using remote sensing, and uses satellite imagery to make recommendations for future sampling strategies. Numéro de notice : A2021-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13224625 Date de publication en ligne : 17/11/2021 En ligne : https://doi.org/10.3390/rs13224625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99022
in Remote sensing > vol 13 n° 22 (November-2 2021) . - n° 4625[article]Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)
PermalinkAccuracy assessment of RTK-GNSS equipped UAV conducted as-built surveys for construction site modelling / Sander Varbla in Survey review, Vol 53 n° 381 (November 2021)
PermalinkAccurate mapping method for UAV photogrammetry without ground control points in the map projection frame / Jianchen Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)
PermalinkAutomatic tuning of segmentation parameters for tree crown delineation with VHR imagery / Camile Sothe in Geocarto international, vol 36 n° 19 ([01/11/2021])
PermalinkBagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)
PermalinkLa campagne Caddiwa dans la région des îles du Cap-Vert / Cyrille Flamant in La Météorologie, n° 115 (2021)
PermalinkDownscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)
PermalinkEfficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine / Yongjing Mao in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)
PermalinkFeature matching for multi-epoch historical aerial images / Lulin Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)
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PermalinkFully automated pose estimation of historical images in the context of 4D geographic information systems utilizing machine learning methods / Ferdinand Maiwald in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)
PermalinkIdentifying surface urban heat island drivers and their spatial heterogeneity in China’s 281 cities: An empirical study based on multiscale geographically weighted regression / Lu Niu in Remote sensing, vol 13 n° 21 (November-1 2021)
PermalinkLand subsidence in Beijing’s sub-administrative center and its relationship with urban expansion inferred from Sentinel-1/2 observations / Jin Cao in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
PermalinkPermalinkMulti-objective CNN-based algorithm for SAR despeckling / Sergio Vitale in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)
PermalinkMulti-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
PermalinkA novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)
PermalinkA parameterization of the cloud scattering polarization signal derived from GPM observations for microwave fast radative transfer models / Victoria Sol Galligani in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)
PermalinkPersistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images / Hari Shankar in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)
PermalinkA repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France / Arnaud Cerbelaud in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)
PermalinkRobust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features / Bai Zhu in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)
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