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Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (October 2020)
[article]
Titre : Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Cuizhen Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 329 - 342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contour
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] jeu de données
[Termes IGN] Kiangsi (Chine)
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] taille du jeu de donnéesRésumé : (auteur) Land cover data is an inventory of objects on the Earth’s surface, which is often derived from remotely sensed imagery. Deep Convolutional Neural Network (DCNN) is a competitive method in image semantic segmentation. Some scholars argue that the inadequacy of training set is an obstacle when applying DCNNs in remote sensing image segmentation. While existing land cover data can be converted to large training sets, the size of training data set needs to be carefully considered. In this paper, we used different portions of a high-resolution land cover map to produce different sizes of training sets to train DCNNs (SegNet and U-Net) and then quantitatively evaluated the impact of training set size on the performance of the trained DCNN. We also introduced a new metric, Edge-ratio, to assess the performance of DCNN in maintaining the boundary of land cover objects. Based on the experiments, we document the relationship between the segmentation accuracy and the size of the training set, as well as the nonstationary accuracies among different land cover types. The findings of this paper can be used to effectively tailor the existing land cover data to training sets, and thus accelerate the assessment and employment of deep learning techniques for high-resolution land cover map extraction. Numéro de notice : A2020-800 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1803402 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1803402 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96723
in Annals of GIS > vol 26 n° 4 (October 2020) . - pp 329 - 342[article]Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])
[article]
Titre : Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands Type de document : Article/Communication Auteurs : Bappa Das, Auteur ; Rabi N. Sahoo, Auteur ; Sourabh Pargal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1415 - 1432 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] blé (céréale)
[Termes IGN] canopée
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] réflectance spectrale
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] séparateur à vaste marge
[Termes IGN] spectroradiomètreRésumé : (auteur) Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors. Numéro de notice : A2020-607 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581271 Date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95967
in Geocarto international > vol 35 n° 13 [01/10/2020] . - pp 1415 - 1432[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2020101 RAB Revue Centre de documentation En réserve L003 Disponible Ground-based remote sensing of forests exploiting GNSS signals / Leila Guerriero in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
[article]
Titre : Ground-based remote sensing of forests exploiting GNSS signals Type de document : Article/Communication Auteurs : Leila Guerriero, Auteur ; Francisco Martin, Auteur ; Antonio Mollfulleda, Auteur Année de publication : 2020 Article en page(s) : pp 6844 - 6860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] atténuation du signal
[Termes IGN] bande L
[Termes IGN] bande P
[Termes IGN] biomasse aérienne
[Termes IGN] canopée
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] Leaf Area Index
[Termes IGN] polarisation
[Termes IGN] Populus (genre)
[Termes IGN] réseau neuronal artificiel
[Termes IGN] signal GNSSRésumé : (auteur) The estimation of aboveground biomass is commonly recognized for global relevance because of the vegetation role in the carbon cycle. Both active and passive microwave sensors can significantly contribute to this goal because of their high sensitivity to water content and high penetration at lower frequencies (L-/P-bands). In particular, Global Navigation Satellite Systems (GNSSs) are recently receiving increasing interest as source of opportunity to be employed as illuminator for L-band remote sensing, since they could provide low-cost sensors for nondestructive forest biomass estimation over large areas. In this article, we suggest a method to extract forest information using the GNSS direct signals collected in clear sky and below the vegetation canopy at both circular polarizations. An experimental campaign, carried out in the framework of an European Space Agency (ESA) project, was conducted over three poplar forests with different biomass to verify the feasibility of this technique. The relationships between the GNSS measurements and the tree parameters were first assessed and then interpreted and supported by statistical analysis and a theoretical model. The signal collected under the canopy is affected by attenuation and depolarization with respect to the one collected in open air, and this article demonstrated that both direct line-of-sight propagation and volume scattering play a role in the signal magnitude and its fluctuation in time. Although the experimental data set is limited in size and environmental conditions, two inversion algorithms were also tested with the encouraging retrieval results. Numéro de notice : A2020-585 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976899 Date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95913
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6844 - 6860[article]Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal / Raghu Nadimpalli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
[article]
Titre : Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal Type de document : Article/Communication Auteurs : Raghu Nadimpalli, Auteur ; Akhil Srivastava, Auteur ; V. S. Prasad, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6945 - 6957 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Bengale, golfe du
[Termes IGN] cyclone
[Termes IGN] image INSAT-VHRR
[Termes IGN] interpolation
[Termes IGN] matrice de covariance
[Termes IGN] modèle de transfert radiatif
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] radiance
[Termes IGN] zone intertropicaleRésumé : (auteur) This is the first study concerning the assimilation of the INSAT-3D/3DR radiance in the Hurricane Weather Research and Forecasting (HWRF) model and assesses its credibility to improve track, intensity, and precipitation forecasts of tropical cyclone (TC) Titli that occurred over the Bay of Bengal (BoB), which showed rapid intensification (RI) and weakening through its lifetime. The inbuilt Gridpoint Statistical Interpolation (GSI) method is used with a 3-D variational (3DVAR) configuration. Three sets of numerical experiments such as control (CNTL) (no assimilation), Global Telecommunication System (GTS) (observations from GTS network), and INSAT-3D/3DR (INSAT-3D/3DR sounder radiance data and GTS observations) were carried out with seven different initializations. The radiance analysis reproduced the initial vortex and the prominent synoptic scale features associated with TC Titli. The average root-mean-square errors (RMSE) of the analysis were relatively lower in the INSAT-3D/3DR compared to the CNTL and GTS. The HWRF performance is enhanced for track simulation, with improvements in mean landfall position errors by 40%–70% and 26%–52% for the INSAT-3D/3DR and GTS runs, respectively. The assimilation of radiance data has a positive impact on the simulation of warm core and thermodynamic structures, which has led to a more accurate intensity prediction (by 30–47%) over the CNTL. The assimilation run could realistically simulate the RI and weakening phases of the TC. A cold dry air intrusion is also observed when associated with the weakening. The study highlights the need to incorporate INSAT-3D/3DR radiances for improved TC predictions over the BoB basin. Numéro de notice : A2020-587 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2978211 Date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2978211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95915
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6945 - 6957[article]A machine learning framework for estimating leaf biochemical parameters from its spectral reflectance and transmission measurements / Bikram Koirala in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
[article]
Titre : A machine learning framework for estimating leaf biochemical parameters from its spectral reflectance and transmission measurements Type de document : Article/Communication Auteurs : Bikram Koirala, Auteur ; Zohreh Zahiri, Auteur ; Paul Scheunders, Auteur Année de publication : 2020 Article en page(s) : pp 7393 - 7405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] biochimie
[Termes IGN] diagnostic foliaire
[Termes IGN] feuille (végétation)
[Termes IGN] indice de végétation
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] processus gaussien
[Termes IGN] réflectance spectrale
[Termes IGN] régression
[Termes IGN] teneur en chlorophylle des feuillesRésumé : (auteur) Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll a and b, carotenoid, anthocyanin, brown pigment, leaf water content, and leaf mass per area for the quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows us to use the full wavelength range (400–2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relationship between estimated parameters and the reflectance/transmission spectra is unclear. In this article, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral data set to a data set that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on Gaussian processes (GPs) and kernel ridge regression (KRR) are proposed. As an alternative, mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX data set. Numéro de notice : A2020-589 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2982263 Date de publication en ligne : 02/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2982263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95919
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7393 - 7405[article]La méthode de la photo-interview à partir de la photographie aérienne : Le cas d’un bidonville à Nanterre dans les années 1960 / Laetitia Delavoipiere in EchoGeo, n° 54 (octobre - décembre 2020)PermalinkA preliminary exploration of the cooling effect of tree shade in urban landscapes / Qiuyan Yu in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)PermalinkSpatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris / Xu Ge in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)PermalinkUncertainty of forested wetland maps derived from aerial photography / Stephen P. Prisley in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 10 (October 2020)PermalinkWide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October-1 2020)PermalinkUse of visible and near-infrared reflectance spectroscopy models to determine soil erodibility factor (K) in an ecologically restored watershed / Qinghu Jiang in Remote sensing, vol 12 n° 18 (September-2 2020)PermalinkAnalysis of chlorophyll concentration in potato crop by coupling continuous wavelet transform and spectral variable optimization / Ning Liu in Remote sensing, vol 12 n° 17 (September-1 2020)PermalinkApplying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)PermalinkArctic tsunamis threaten coastal landscapes and communities – survey of Karrat Isfjord 2017 tsunami effects in Nuugaatsiaq, western Greenland / Mateusz C. Strzelecki in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkCombining optical and radar satellite image time series to map natural vegetation: savannas as an example / Maylis Lopes in Remote sensing in ecology and conservation, vol 6 n° 3 (September 2020)Permalink