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Geometric multi-wavelet total variation for SAR image time series analysis / Abdourrahmane M. Atto (2018)
Titre : Geometric multi-wavelet total variation for SAR image time series analysis Type de document : Article/Communication Auteurs : Abdourrahmane M. Atto, Auteur ; Anoumou Kemavo, Auteur ; Jean-Paul Rudant , Auteur ; Grégoire Mercier, Auteur Editeur : Chambéry : Université de Savoie Année de publication : 2018 Conférence : FUSION 2018, 21th International Conference on Information Fusion 10/07/2018 13/07/2018 Cambridge Royaume-Uni Proceedings IEEE Projets : PHOENIX / Atto, Abdourrahmane M. Importance : pp Note générale : bibliographie
Projet PHOENIX ANR-15-CE23-00Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Amazonie
[Termes IGN] forêt tropicale
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] Oyapoc (fleuve)
[Termes IGN] série temporelleRésumé : (auteur) A time series issued from modern synthetic aperture radar satellite imaging sensors is a huge dataset composed by many hundreds of million pixels when observing large-scale earth structures such as big forests or glaciers. A concise monitoring of these large scale structures for anomaly spotting thus requires loading and analyzing huge spatio/polarimetric multi-temporal image series. The contributions of the present paper for the sake of parsimonious analysis of such huge datasets are associated with a framework having two main processing stages. The first stage is the derivation of an index called geometric multi-wavelet total variation for fast and robust anomaly spotting. This index is useful for identifying significant abnormal patterns appearing as geo-spatial non-stationarities in multi-wavelet total variation map. The second stage consists in the proposal of a concise asymmetric multi-date change information matrix on regions associated with significant multi-wavelet total variations. This stage is necessary for a fine characterization of change impacts on existing geo-spatial structures. Experimental tests based on Sentinel-1 data show relevant results on a wide Amazonian forest surrounding the Franco-Brazilian Oyapock Bridge. Numéro de notice : C2018-125 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.23919/ICIF.2018.8455223 Date de publication en ligne : 06/09/2018 En ligne : https://doi.org/10.23919/ICIF.2018.8455223 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100016 A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)
[article]
Titre : A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning Type de document : Article/Communication Auteurs : Rasmus M. Houborg, Auteur ; Matthew F. McCabe, Auteur Année de publication : 2018 Article en page(s) : pp 173 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image RapidEye
[Termes IGN] Leaf Area Index
[Termes IGN] réflectance de surface
[Termes IGN] régressionRésumé : (Auteur) With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory ‘predictor’ variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes. Numéro de notice : A2018-070 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89428
in ISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 173 - 188[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018012 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2018013 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Un inventaire forestier multisource pour la gestion des territoires / Dinesh Babu Irulappa-Pillai-Vijayakumar (2018)
contenu dans 27èmes Journées de la Recherche de l'IGN / Journées Recherche de l'IGN 2018, 27es Journées (22 - 23 mars 2018; Cité Descartes, Champs-sur-Marne, France) (2018)
Titre : Un inventaire forestier multisource pour la gestion des territoires Type de document : Article/Communication Auteurs : Dinesh Babu Irulappa-Pillai-Vijayakumar , Auteur ; Jean-Pierre Renaud , Auteur ; François Morneau , Auteur ; Cédric Vega , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2018 Projets : DIABOLO / Packalen, Tuula Conférence : Journées Recherche de l'IGN 2018, 27es Journées 22/03/2018 23/03/2018 Champs-sur-Marne France programme sans actes Langues : Français (fre) Descripteur : [Termes IGN] carte forestière
[Termes IGN] Centre (France administrative)
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données multisources
[Termes IGN] estimation statistique
[Termes IGN] image aérienne
[Termes IGN] image Landsat-TM
[Termes IGN] indice de végétation
[Termes IGN] inférence statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de terrain
[Termes IGN] Orléans, forêt domaniale d' (Loiret)
[Termes IGN] plus proche voisin, algorithme du
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) L’inventaire forestier national permet d’obtenir un portrait précis de l’état des forêts de France métropolitaine. Mais l’échantillon de points est rapidement trop faible pour fournir des estimations statistiques ayant la précision nécessaire à l’échelle des territoires, où s’exercent les décisions. Les méthodes d’inventaire forestier multisource ont été développées afin de résoudre ce problème, sur des territoires circonscrits, en associant aux mesures de terrain des données auxiliaires, généralement issues de la télédétection. Le projet IFM-GT vise à développer et adapter un tel système d’inventaire forestier multisource en France. Ses sorties pourront contribuer à l’élaboration de stratégies de gestion pour des territoires forestiers particuliers, dans le cadre d’étude de ressources, par exemple. Le système s’appuie sur les mesures terrain de l’inventaire, la carte forestière, des images de télédétection 2D et 3D, et des méthodes statistiques de type k plus proches voisins (k-nn). Cette présentation introduira les concepts d’inventaire multisource et présentera la méthodologie développée et testée sur un territoire forestier de la région Centre. Numéro de notice : C2018-033 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91131 Documents numériques
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Un inventaire forestier multisource... - diaporama de présentationAdobe Acrobat PDF Learning multiscale deep features for high-resolution satellite image scene classification / Qingshan Liu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)
[article]
Titre : Learning multiscale deep features for high-resolution satellite image scene classification Type de document : Article/Communication Auteurs : Qingshan Liu, Auteur ; Renlong Hang, Auteur ; Huihui Song, Auteur ; Zhi Li, Auteur Année de publication : 2018 Article en page(s) : pp 117 - 126 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] image satellite
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) In this paper, we propose a multiscale deep feature learning method for high-resolution satellite image scene classification. Specifically, we first warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multiscale satellite images are fed into their corresponding SPP-nets, respectively, to extract multiscale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult data sets show that the proposed method achieves favorable performance compared with other state-of-the-art methods. Numéro de notice : A2018-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2743243 Date de publication en ligne : 13/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2743243 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89842
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 117 - 126[article]Mapping grassland management intensity using Sentinel-2 satellite data / Marijke Elisabeth Bekkema in GI Forum, vol 2018 n° 1 ([01/01/2018])
[article]
Titre : Mapping grassland management intensity using Sentinel-2 satellite data Type de document : Article/Communication Auteurs : Marijke Elisabeth Bekkema, Auteur ; Marieke Eleveld, Auteur Année de publication : 2018 Article en page(s) : pp 194 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Aves
[Termes IGN] biodiversité
[Termes IGN] habitat animal
[Termes IGN] image Sentinel-MSI
[Termes IGN] prairieRésumé : (auteur) For the conservation of biodiversity in general and the monitoring of meadow birds in particular, actual grassland - use intensity maps are highly desirable. A method to map and assess grassland management intensity was developed using C5.0 decision tree classification on Sentinel-2 satellite data. Monoculture and extensively managed grasslands on both peat and clay soils could be accurately detected at parcel level in Friesland, the Netherlands. Field - survey - based validation returned an overall classification accuracy of 84.3% (KHAT 0.65). The Sentinel-2 Red-Edge Position vegetation index was found to be a good indicator of fertilization. Availability of springtime imagery, preferably acquired in April before the first mowing date, is essential for accurate classification. The spectral responses of grassland types on peat and clay soils differ significantly. Hence, successful classification requires training data for both soil types. The resulting grassland management map was used to assess the distribution of meadow bird nests. Redshank (79%) and godwit (77%) in particular choose to breed on extensive parcels. With the increasing availability of satellite imagery, remote sensing techniques can be used to monitor agri-environmental measures (at parcel and landscape scale) that impact the conservation of grassland biodiversity. Numéro de notice : A2018-301 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1553/giscience2018_01_s194 En ligne : http://dx.doi.org/10.1553/giscience2018_01_s194 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90399
in GI Forum > vol 2018 n° 1 [01/01/2018] . - pp 194 - 213[article]Mise en évidence de l’activité récente des failles du bassin de Naryn (Kyrgyzstan) à partir de données photogrammétriques Pléiades et drone : un nouvel apport pour l’aléa sismique / Aurélie Médard (2018)PermalinkModélisation spatio-temporelle multi-niveau à base d'ontologies pour le suivi de la dynamique en imagerie satellitaire / Fethi Ghazouani (2018)PermalinkPermalinkPermalinkPotential and limits of Sentinel-1 data for small alpine glaciers monitoring / Matthias Jauvin (2018)PermalinkPermalinkQGIS in Remote Sensing, Volume 2. 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