Descripteur
Documents disponibles dans cette catégorie (34)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
National scale mapping of larch plantations for Wales using the Sentinel-2 data archive / Suvarna M. Punalekar in Forest ecology and management, vol 501 (December-1 2021)
[article]
Titre : National scale mapping of larch plantations for Wales using the Sentinel-2 data archive Type de document : Article/Communication Auteurs : Suvarna M. Punalekar, Auteur ; Carole Planque, Auteur ; Richard M. Lucas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] carte forestière
[Termes IGN] coupe rase (sylviculture)
[Termes IGN] gestion forestière
[Termes IGN] image infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Larix decidua
[Termes IGN] maladie phytosanitaire
[Termes IGN] modélisation de la forêt
[Termes IGN] Pays de Galles
[Termes IGN] surveillance forestièreRésumé : (auteur) Accurate spatial information regarding forest types and tree species is immensely important for efficient forest management strategies. In the UK and particularly in Wales, creating a spatial inventory of larch (Larix sps.) plantations that encompasses both the public and private forests has become one of the highest priorities of woodland management policies, particularly given the need to respond to the rapid spread of Phytophthora ramorum fungal disease. For directing disease control measures, national scale, regularly updated mapping of larch distributions is essential. In this study, we applied a ExtraTree classifier machine learning algorithm to multi-year (June 2015 and December 2019) multi-path composites of vegetation indices derived from 10 m Sentinel-2 satellite data (spectral range used in this study: 490–2190 nm) to map the extent of larch plantations across Wales. For areas identified as woody vegetation, areas under larch plantations were associated with a needle-leaved leaf type and deciduous phenology, allowing differentiation from broad-leaved deciduous and needle-leaved evergreen types. The model accuracies for validation, which included overall accuracy, producer’s and user’s accuracies, exceeded 95% and the F1-score was greater than 0.97 for all forest types. Comparison against an independent reference dataset indicated all map accuracies above 90% (F1-score higher than 0.92) with the lowest value being 90.3% for the producer’s accuracy for larch. Short wave infrared and red-edge based indices were particularly useful for discriminating larch from other forest types. Capacity for updating information on clear-felling of larch stands through annual updates of a woody mask was also introduced. The resulting maps of larch plantations for Wales are the most current for Wales covering public as well as private woodlands and can be routinely updated. The classification approach has potential to be transferred to a wider geographical area given the availability of open-source multi-year Sentienl-2 datasets and robust calibration datasets. Numéro de notice : A2021-741 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119679 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98657
in Forest ecology and management > vol 501 (December-1 2021) . - n° 119679[article]Bagging 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)
[article]
Titre : Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation Type de document : Article/Communication Auteurs : Hamid Jafarzadeh, Auteur ; Masoud Mahdianpari, Auteur ; Eric Gill, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] boosting adapté
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données polarimétriques
[Termes IGN] ensachage
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image ROSISRésumé : (auteur) In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data. Numéro de notice : A2021-823 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13214405 Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.3390/rs13214405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98938
in Remote sensing > vol 13 n° 21 (November-1 2021) . - n° 4405[article]A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area Type de document : Article/Communication Auteurs : Myung-Jin Jun, Auteur Année de publication : 2021 Article en page(s) : pp 2149 - 2167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] arbre de décision
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] Séoul
[Termes IGN] zone urbaineRésumé : (auteur) This study compares the performance of gradient boosting decision tree (GBDT), artificial neural networks (ANNs), and random forests (RF) methods in LUC modeling in the Seoul metropolitan area. The results of this study showed that GBDT and RF have higher predictive power than ANN, indicating that tree-based ensemble methods are an effective technique for LUC prediction. Along with the outstanding predictive performance, the DT-based ensemble models provide insights for understanding which factors drive LUCs in complex urban dynamics with the relative importance and nonlinear marginal effects of predictor variables. The GBDT results indicate that distance to the existing residential site has the highest contribution to urban land use conversion (30.4% of the relative importance), while other significant predictor variables were proximity to industrial and public sites (combined 32.3% of relative importance). New residential development is likely to be adjacent to existing residential sites, but nonresidential development occurs at a distance (about 600 m) from such sites. The distance to the central business district (CBD) had increasing marginal effects on residential land use conversion, while no significant pattern was found for nonresidential land use conversion, indicating that Seoul has experienced more population suburbanization than employment decentralization. Numéro de notice : A2021-756 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887490 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887490 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98771
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2149 - 2167[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Diffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)
[article]
Titre : Diffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression Type de document : Article/Communication Auteurs : Forrest Corcoran, Auteur ; Christopher E. Parrish, Auteur Année de publication : 2021 Article en page(s) : pp 831 - 840 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] capteur spatial
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forme d'onde
[Termes IGN] littoral
[Termes IGN] modèle de régression
[Termes IGN] semis de points
[Termes IGN] turbidité des eauxRésumé : (Auteur) This study investigates a new method for measuring water turbidity—specifically, the diffuse attenuation coefficient of downwelling irradiance Kd —using data from a spaceborne, green-wavelength lidar aboard the National Aeronautics and Space Administration's ICESat-2 satellite. The method enables us to fill nearshore data voids in existing Kd data sets and provides a more direct measurement approach than methods based on passive multispectral satellite imagery. Furthermore, in contrast to other lidar-based methods, it does not rely on extensive signal processing or the availability of the system impulse response function, and it is designed to be applied globally rather than at a specific geographic location. The model was tested using Kd measurements from the National Oceanic and Atmospheric Administration's Visible Infrared Imaging Radiometer Suite sensor at 94 coastal sites spanning the globe, with Kd values ranging from 0.05 to 3.6 m –1 . The results demonstrate the efficacy of the approach and serve as a benchmark for future machine-learning regression studies of turbidity using ICESat-2. Numéro de notice : A2021-896 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00013R2 Date de publication en ligne : 01/11/2021 En ligne : https://doi.org/10.14358/PERS.21-00013R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99272
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 11 (November 2021) . - pp 831 - 840[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021111 SL Revue Centre de documentation Revues en salle Disponible Two hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])
[article]
Titre : Two hidden layer neural network-based rotation forest ensemble for hyperspectral image classification Type de document : Article/Communication Auteurs : Laxmi Narayana Eeti, Auteur ; Krishna Mohan Buddhiraju, Auteur Année de publication : 2021 Article en page(s) : pp 1820 - 1837 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre de décision
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] ensachage
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image ROSIS
[Termes IGN] Perceptron multicouche
[Termes IGN] précision de la classification
[Termes IGN] réseau neuronal profond
[Termes IGN] Rotation Forest classificationRésumé : (auteur) Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy for a given training sample set and image data, owing to the inherent disadvantages in decision trees, namely myopic, replication and fragmentation problem. To improve performance of Rotation Forest technique, we propose to utilize two-hidden-layered-feedforward neural network as base classifier instead of decision tree. We examine the classification performance of proposed model under two situations, namely when free network parameters are maintained the same across all ensemble components and otherwise. The proposed model, where each component is initialized with different pair of initial weights and bias, performs better than decision tree-based Rotation Forest on three different Hyperspectral sensor datasets – AVIRIS, ROSIS and Hyperion. Improvements in classification accuracy are above 2% and up to 3% depending upon dataset. Also, the proposed model achieves improvement in accuracy over Random Forest in the range 4.2–8.8%. Numéro de notice : A2021-581 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678680 Date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678680 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98193
in Geocarto international > vol 36 n° 16 [01/09/2021] . - pp 1820 - 1837[article]An adaptive filtering algorithm of multilevel resolution point cloud / Youyuan Li in Survey review, Vol 53 n° 379 (July 2021)PermalinkMachine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices / Linchuan Yang in Annals of GIS, vol 27 n° 3 (July 2021)PermalinkPredicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)PermalinkThe delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkPermalinkExtraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkPermalinkA method of hydrographic survey technology selection based on the decision tree supervised learning / Ivana Golub Medvešek (2021)PermalinkForêt d'arbres aléatoires et classification d'images satellites : relation entre la précision du modèle d'entraînement et la précision globale de la classification / Aurélien N.G. Matsaguim in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)PermalinkPairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets / Yusheng Xu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkVoxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods / Florent Poux in ISPRS International journal of geo-information, vol 8 n° 5 (May 2019)PermalinkCentral place indexing : hierarchical linear indexing systems for mixed-aperture hexagonal discrete global grid systems / Kevin Sahr in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkGeospatial data organization methods with emphasis on aperture-3 hexagonal discrete global grid systems / Ali Mahdavi Amiri in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkA derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds / Fan Xue in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkSpatial data management in apache spark: the GeoSpark perspective and beyond / Jia Yu in Geoinformatica, vol 23 n° 1 (January 2019)PermalinkRoad safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree / José Antonio Martin-Jimenez in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkSpatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkMining and visual exploration of closed contiguous sequential patterns in trajectories / Can Yang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkA voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation / Yusheng Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkAn object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkLocal curvature entropy-based 3D terrain representation using a comprehensive Quadtree / Giyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkA spatio-temporal index for aerial full waveform laser scanning data / Debra F. Laefer in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkAnalyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2 / François Messner in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)PermalinkSemantic enrichment of octree structured point clouds for multi‐story 3D pathfinding / Florian W. Fichtner in Transactions in GIS, vol 22 n° 1 (February 2018)PermalinkAbove-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China / Ran Jing in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)PermalinkGlobal, dense multiscale reconstruction for a billion points / Benjamin Ummenhofer in International journal of computer vision, vol 125 n° 1-3 (December 2017)PermalinkA morphologically preserved multi-resolution TIN surface modeling and visualization method for virtual globes / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkVariable-scale maps in real-time generalisation using a quadtree data structure and space deforming algorithms / Pia Bereuter in International journal of cartography, vol 3 n° 1 (June 2017)PermalinkPredicting the encoding of secondary diagnoses. An experience based on decision trees / Ghazar Chahbandarian in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 22 n° 2 (mars - avril 2017)Permalink