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Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours / Amir Hossein Safaie in ISPRS Journal of photogrammetry and remote sensing, Vol 174 (April 2021)
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Titre : Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours Type de document : Article/Communication Auteurs : Amir Hossein Safaie, Auteur ; Heidar Rastiveis, Auteur ; Alireza Shams, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 19 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] arbre remarquable
[Termes descripteurs IGN] détection d'arbres
[Termes descripteurs IGN] détection de contours
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] inventaire
[Termes descripteurs IGN] sécurité routière
[Termes descripteurs IGN] semis de points
[Termes descripteurs IGN] tessellation
[Termes descripteurs IGN] transformation de HoughRésumé : (auteur) Trees are important road-side objects, and their geometric information plays an essential role in road studies and safety analyses. This paper proposes an efficient method for the automated creation of a road-side tree inventory using Mobile Terrestrial Lidar System (MTLS) point clouds. In the proposed method ground points are filtered through preprocessing to reduce processing time. Next, tree trunks are detected by performing a Hough Transform (HT) algorithm on several generated raster images from the point clouds. By initiating an approximate area of a tree’s foliage through a Voronoi Tessellation (VT) algorithm, the accurate boundary of the foliage is identified by applying Active Contour (AC) models. By extracting the points within this foliage boundary the geometric characteristics of each tree are obtained. This method was evaluated with two sample point clouds from different MTLS systems, and the algorithm correctly extracted all of the trees from both datasets. Additionally, comparing the calculated parameters with manually observed measures, the accuracy of the obtained geometric parameters were promising. Numéro de notice : A2021-206 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.026 date de publication en ligne : 14/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.026 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97183
in ISPRS Journal of photogrammetry and remote sensing > Vol 174 (April 2021) . - pp 19 - 34[article]A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, Vol 174 (April 2021)
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Titre : A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; Diogo Nunes Gonçalves, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] carte agricole
[Termes descripteurs IGN] Citrus sinensis
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] comptage
[Termes descripteurs IGN] cultures
[Termes descripteurs IGN] détection d'objet
[Termes descripteurs IGN] extraction de la végétation
[Termes descripteurs IGN] gestion durable
[Termes descripteurs IGN] image captée par drone
[Termes descripteurs IGN] maïs (céréale)
[Termes descripteurs IGN] rendement agricoleRésumé : (auteur) Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch; which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-measure equal to 0.876. These results were superior to the results from other deep networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and dataset. For the plantation-row detection, our approach returned precision, recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test the robustness of our model with a different type of agriculture, we performed the same task in the citrus orchard dataset. It returned an MAE equal to 1.409 citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and F-measure of 0.965. For the citrus plantation-row detection, our approach resulted in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964, respectively. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops. The method proposed here may be applied to future decision-making models and could contribute to the sustainable management of agricultural systems. Numéro de notice : A2021-205 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.024 date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97171
in ISPRS Journal of photogrammetry and remote sensing > Vol 174 (April 2021) . - pp 1 - 17[article]Multi-GNSS real-time precise clock estimation considering the correction of inter-satellite code biases / Liang Chen in GPS solutions, vol 25 n° 2 (April 2021)
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Titre : Multi-GNSS real-time precise clock estimation considering the correction of inter-satellite code biases Type de document : Article/Communication Auteurs : Liang Chen, Auteur ; Min Li, Auteur ; Ying Zhao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : 17 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes descripteurs IGN] correction
[Termes descripteurs IGN] décalage d'horloge
[Termes descripteurs IGN] erreur systématique inter-systèmes
[Termes descripteurs IGN] phase
[Termes descripteurs IGN] positionnement par BeiDou
[Termes descripteurs IGN] positionnement par Galileo
[Termes descripteurs IGN] positionnement par GLONASS
[Termes descripteurs IGN] positionnement par GNSS
[Termes descripteurs IGN] positionnement par GPS
[Termes descripteurs IGN] récepteur GNSS
[Termes descripteurs IGN] temps réelRésumé : (Auteur) For reasons mostly related to chip shape distortions, global navigation satellite system (GNSS) observations are corrupted by receiver-dependent biases. These are often stable in the long term, though numerically different depending on the signal frequency, satellite system and receiver manufacturer. Based on the mixed-differenced model combining undifferenced pseudorange with epoch-differenced carrier phase observations, we present a multi-GNSS real-time precise clock estimation model considering correction of inter-satellite code biases (ISCBs). Pre-estimated receiver-dependent ISCB corrections are introduced to correct the inter-receiver, inter-satellite and inter-system biases largely. Then the number of estimated parameters is reduced to a manageable level for real-time estimation. Comparisons with post-processed data show that compared to undifferenced, epoch-differenced and non-bias-corrected mixed-differenced models, the proposed bias-corrected model can greatly reduce the precise clock offset systematic biases, especially for GLONASS and BeiDou. The test results show the root mean square data reductions are improved by up to 96% for GLONASS, 78% for BeiDou and 40% for GPS and Galileo. Numéro de notice : A2021-092 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01065-z date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.1007/s10291-020-01065-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96883
in GPS solutions > vol 25 n° 2 (April 2021) . - 17 p.[article]sing data usinAutomatic atmospheric correction for shortwave hyperspectral remote seng a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, Vol 174 (April 2021)
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Titre : sing data usinAutomatic atmospheric correction for shortwave hyperspectral remote seng a time-dependent deep neural network Type de document : Article/Communication Auteurs : Jian Sun, Auteur ; Fangcao Xu, Auteur ; Guido Cervone, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 117 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] correction atmosphérique
[Termes descripteurs IGN] détection de cible
[Termes descripteurs IGN] image hyperspectrale
[Termes descripteurs IGN] modèle de transfert radiatif
[Termes descripteurs IGN] rayonnement solaire
[Termes descripteurs IGN] réflectivitéRésumé : (auteur) Atmospheric correction is an essential step in hyperspectral imaging and target detection from spectrometer remote sensing data. State-of-the-art atmospheric correction approaches either require extensive filed experiments or prior knowledge of atmospheric characteristics to improve the predicted accuracy, which are computational expensive and unsuitable for real time application. To take full advantages of remote sensing observation in quickly and reliably acquiring data for a large area, an automatic and efficient processing tool is required for atmospheric correction. In this paper, we propose a time-dependent neural network for automatic atmospheric correction and target detection using multi-scan hyperspectral data under different elevation angles. In addition to the total radiance, the collection day and time are also incorporated to improve the time-dependency of the network and represent the seasonal and diurnal characteristics of atmosphere and solar radiation. Results show that the proposed network has the capacity to accurately provide atmospheric characteristics and estimate precise reflectivity spectra with 95,72% averaged accuracy for different materials, including vegetation, sea ice, and ocean. Additional experiments are designed to investigate the network’s temporal dependency and performance on missing data. The error analysis confirms that our proposed network is capable of estimating atmospheric characteristics under both seasonally and diurnally varying environments and handling the influence of missing data. Both the predicted results and error analysis are promising and demonstrate that our network has the ability of providing accurate atmospheric correction and target detection in real time. Numéro de notice : A2021-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.007 date de publication en ligne : 24/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97186
in ISPRS Journal of photogrammetry and remote sensing > Vol 174 (April 2021) . - pp 117 - 131[article]Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)
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Titre : Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine Type de document : Article/Communication Auteurs : Dong Liang, Auteur ; Huadong Guo, Auteur ; Lu Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112318 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] albedo
[Termes descripteurs IGN] Antarctique
[Termes descripteurs IGN] calotte glaciaire
[Termes descripteurs IGN] changement climatique
[Termes descripteurs IGN] coefficient de rétrodiffusion
[Termes descripteurs IGN] distribution spatiale
[Termes descripteurs IGN] fonte des glaces
[Termes descripteurs IGN] Google Earth Engine
[Termes descripteurs IGN] image radar moirée
[Termes descripteurs IGN] image Sentinel-SAR
[Termes descripteurs IGN] montée du niveau de la mer
[Termes descripteurs IGN] série temporelleRésumé : (auteur) The Antarctic ice sheet is an important mass of glacier ice. It is particularly sensitive to climate change, and the flow of Antarctica's inland glaciers into the sea, accelerated by collapsing ice shelves, threatens global sea level rise. The amount of snowmelt on the surface of the ice sheet is an important metric for accurately assessing surface material loss and albedo change, which affect the stability of the ice sheet. This study proposes a framework for quickly extracting time-series freeze-thaw information at the continental scale and 40 m resolution by taking advantage of the huge amount of synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites over the Antarctic, available for rapid processing on Google Earth Engine. Co-orbit normalization is used in the proposed framework to establish a unified standard of judgement by reducing the variations in the backscattering coefficient introduced by observation geometry, terrain fluctuations, and melt conditions between images acquired at different times. We implemented the framework to produce a massive dataset of both monthly freeze-thaw information over the Antarctic and higher temporal resolution freeze-thaw information for the Larsen C ice shelf from 2015 to 2019, with overall accuracies of 93% verified by a manual visual interpretation method and 84% evaluated from automatic weather station temperatures. Due to its effectiveness and robustness, the framework can be used to analyse the spatiotemporal distribution of snowmelt, the change in melt area, and anomalous melt events in Antarctica, especially those in Larsen C caused by foehn wind. Numéro de notice : A2021-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112318 date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112318 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97117
in Remote sensing of environment > Vol 256 (April 2020) . - n° 112318[article]Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide / Chaoyang Niu in ISPRS Journal of photogrammetry and remote sensing, Vol 174 (April 2021)
PermalinkBasin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery / Zifeng Wang in Remote sensing of environment, Vol 255 (March 2021)
PermalinkTerrestrial laser scanning intensity captures diurnal variation in leaf water potential / S. Junttila in Remote sensing of environment, Vol 255 (March 2021)
Permalink3D change detection using adaptive thresholds based on local point cloud density / Dan Liu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)
PermalinkAnalysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)
PermalinkAutomating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)
PermalinkComparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model / Francisco Mauro in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)
PermalinkEuropean beech leads to more bioactive humus forms but stronger mineral soil acidification as Norway spruce and Scots pine – Results of a repeated site assessment after 63 and 82 years of forest conversion in Central Germany / Florian Achilles in Forest ecology and management, n°483 ([01/03/2021])
PermalinkGeographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
PermalinkGraph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
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