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Foreword to the special issue on urban remote sensing for smarter cities / Prashanth Reddy Marpu in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 11 n° 8 (August 2018)    

Public [article]  inIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing > vol 11 n° 8 (August 2018) . - pp 2575 - 2577 Titre : Foreword to the special issue on urban remote sensing for smarter cities Type de document : Article/Communication Auteurs : Prashanth Reddy Marpu, Auteur ; Devis Tuia, Auteur ; Clément Mallet , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : pp 2575 - 2577 Langues : Anglais (eng) Numéro de notice : A2018-386 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Nature : Article nature-HAL : ArtSansCL DOI : 10.1109/JSTARS.2018.2863138 Date de publication en ligne : 14/08/2018 En ligne : https://doi.org/10.1109/JSTARS.2018.2863138 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90799 [article]

Documents numériques

en open access Foreword to the special issue ... - pdf éditeur Adobe Acrobat PDF

Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 139 - 149 Titre : Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks Type de document : Article/Communication Auteurs : Rasha Alshehhi, Auteur ; Prashanth Reddy Marpu, Auteur ; Wei Lee Woon, Auteur ; Mauro Dalla Mura, Auteur Année de publication : 2017 Article en page(s) : pp 139 - 149 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] architecture de réseau [Termes IGN] classification par réseau neuronal [Termes IGN] détection du bâti [Termes IGN] extraction automatique [Termes IGN] extraction du réseau routier [Termes IGN] filtrage numérique d'image [Termes IGN] image à haute résolution [Termes IGN] réseau neuronal convolutif [Termes IGN] tachèle [Termes IGN] test de performance [Termes IGN] zone urbaine Résumé : (Auteur) Extraction of man-made objects (e.g., roads and buildings) from remotely sensed imagery plays an important role in many urban applications (e.g., urban land use and land cover assessment, updating geographical databases, change detection, etc). This task is normally difficult due to complex data in the form of heterogeneous appearance with large intra-class and lower inter-class variations. In this work, we propose a single patch-based Convolutional Neural Network (CNN) architecture for extraction of roads and buildings from high-resolution remote sensing data. Low-level features of roads and buildings (e.g., asymmetry and compactness) of adjacent regions are integrated with Convolutional Neural Network (CNN) features during the post-processing stage to improve the performance. Experiments are conducted on two challenging datasets of high-resolution images to demonstrate the performance of the proposed network architecture and the results are compared with other patch-based network architectures. The results demonstrate the validity and superior performance of the proposed network architecture for extracting roads and buildings in urban areas. Numéro de notice : A2017-512 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86458 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017083	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Generalized composite kernel framework for hyperspectral image classification / J. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 9 (September 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 9 (September 2013) . - pp 4816 - 4829 Titre : Generalized composite kernel framework for hyperspectral image classification Type de document : Article/Communication Auteurs : J. Li, Auteur ; Prashanth Reddy Marpu, Auteur ; Antonio Plaza, Auteur ; José M. Bioucas-Dias, Auteur ; et al., Auteur Année de publication : 2013 Conférence : MicroRad 2012, 12th specialist meeting on microwave radiometry and remote sensing applications 05/03/2012 09/03/2012 Rome Italie Proceedings IEEE Article en page(s) : pp 4816 - 4829 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] données localisées [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image ROSIS [Termes IGN] méthode fondée sur le noyau [Termes IGN] régression logistique [Termes IGN] séparateur à vaste marge Résumé : (Auteur) This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios. Numéro de notice : A2013-536 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2230268 En ligne : https://doi.org/10.1109/TGRS.2012.2230268 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32673 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Semisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4032 - 4044 Titre : Semisupervised self-learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Immaculada Dopido, Auteur ; Jun Li, Auteur ; Prashanth Reddy Marpu, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4032 - 4044 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image ROSIS [Termes IGN] régression logistique Résumé : (Auteur) Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the cont- xt of hyperspectral image classification. Numéro de notice : A2013-374 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2228275 En ligne : https://doi.org/10.1109/TGRS.2012.2228275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32512 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013071A	RAB	Revue	Centre de documentation	En réserve L003	Disponible