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Unsupervised domain adaptation for early detection of drought stress in hyperspectral images / P. Schmitter in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 65 - 76 Titre : Unsupervised domain adaptation for early detection of drought stress in hyperspectral images Type de document : Article/Communication Auteurs : P. Schmitter, Auteur ; J. Steinrucken, Auteur ; C. Römer, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 65 - 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] détection automatique [Termes IGN] image hyperspectrale [Termes IGN] stress hydrique Résumé : (Auteur) Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible. Numéro de notice : A2017-536 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86574 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017093	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017092	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Automatic classification of building types in 3D city models: Using SVMs for semantic enrichment of low resolution building data / A. Henn in Geoinformatica, vol 16 n° 2 (April 2012)  

Public [article]  inGeoinformatica > vol 16 n° 2 (April 2012) . - pp 281 - 306 Titre : Automatic classification of building types in 3D city models: Using SVMs for semantic enrichment of low resolution building data Type de document : Article/Communication Auteurs : A. Henn, Auteur ; C. Römer, Auteur ; G. Groger, Auteur ; L. Plumer, Auteur Année de publication : 2012 Article en page(s) : pp 281 - 306 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] attribut sémantique [Termes IGN] bati [Termes IGN] classification automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image à basse résolution [Termes IGN] modèle 3D de l'espace urbain Résumé : (Auteur) This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data. Numéro de notice : A2012-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-011-0131-x Date de publication en ligne : 07/07/2011 En ligne : https://doi.org/10.1007/s10707-011-0131-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31537 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2012021	RAB	Revue	Centre de documentation	En réserve L003	Disponible