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Auteur Walid Ouerghemmi
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Affiner la recherche Interroger des sources externesMultimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)
Titre de série : Multimodal scene understanding: algorithms, applications and deep learning, ch. 11 Titre : Decision fusion of remote-sensing data for land cover classification Type de document : Chapitre/Contribution Auteurs : Arnaud Le Bris Editeur : Londres, New York : Academic Press Année de publication : 2019 Importance : pp 341 - 382 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] fusion de données multisource
[Termes IGN] image à très haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] occupation du sol
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (Auteur) Very high spatial resolution (VHR) multispectral imagery enables a fine delineation of objects and a possible use of texture information. Other sensors provide a lower spatial resolution but an enhanced spectral or temporal information, permitting one to consider richer land cover semantics. So as to benefit from the complementary characteristics of these multimodal sources, a decision late fusion scheme is proposed. This makes it possible to benefit from the full capacities of each sensor, while dealing with both semantic and spatial uncertainties. The different remote-sensing modalities are first classified independently. Separate class membership maps are calculated and then merged at the pixel level, using decision fusion rules. A final label map is obtained from a global regularization scheme in order to deal with spatial uncertainties while conserving the contrasts from the initial images. It relies on a probabilistic graphical model involving a fit-to-data term related to merged class membership measures and an image-based contrast-sensitive regularization term. Conflict between sources can also be integrated into this scheme. Two experimental cases are presented. In the first case one considers the fusion of VHR multispectral imagery with lower spatial resolution hyperspectral imagery for fine-grained land cover classification problem in dense urban areas. In the second case one uses SPOT 6/7 satellite imagery and Sentinel-2 time series to extract urban area footprints through a two-step process: classifications are first merged in order to detect building objects, from which a urban area prior probability is derived and eventually merged to Sentinel-2 classification output for urban footprint detection. Numéro de notice : H2019-002 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1016/B978-0-12-817358-9.00017-2 Date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/B978-0-12-817358-9.00017-2 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93303
Titre : Hyperspectral imagery for environmental urban planning Type de document : Article/Communication Auteurs : Christiane Weber, Auteur ; Rahim Aguejdad, Auteur ; Xavier Briottet Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : HYEP / Weber, Christiane Conférence : IGARSS 2018, IEEE International Geoscience And Remote Sensing Symposium, observing, understanding and forecasting the dynamics of our planet 22/07/2018 27/07/2018 Valencia Espagne Proceedings IEEE Importance : pp 1628 - 1631 Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) A strong intern dynamic characterizes towns, a very high spatial heterogeneity of their elements, their 3D geometric shapes (horizontal and vertical) inducing shadows, and their large variety of materials. These characteristics make the collection of information of land surface properties and urban descriptors more delicate. Due to the enhancement of spatial to deepen the observation of urban areas. Nevertheless, such a type of sensors would not contribute to the characterization of the urban land surface properties (chemical composition of materials, species of vegetation, quality of soils, etc.). They and show great potentials might consider Hyperspectral imagery capacities as providing useful products but it becomes mandatory to define which type of information these different sensors can deliver. The ANR HYEP project has the purpose to demonstrate the benefit of a second generation of hyperspectral space borne mission characterized by a high spatial resolution (8m GSD) and a high temporal revisit. After a detailed description of the motivation of such a proposal, applications are given focused on urban vegetation, sealed and impervious areas, solar panel area estimation. Numéro de notice : C2018-057 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : https://doi.org/10.1109/IGARSS.2018.8519085 Thématique : IMAGERIE/URBANISME Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8519085 Date de publication en ligne : 02/08/2018 En ligne : https://hal-amu.archives-ouvertes.fr/hal-01852844/document Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91372 Documents numériques
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Hyperspectral imagery ... - pdf HALAdobe Acrobat PDFA two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification / Walid Ouerghemmi (2017)
contenu dans ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17 / International society for photogrammetry and remote sensing (1980 -) (2017)
Titre : A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification Type de document : Article/Communication Auteurs : Walid Ouerghemmi Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2017 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 42-1/W1 Projets : HYEP / Weber, Christiane Conférence : ISPRS 2017, Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Annals Importance : pp 167 - 174 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification orientée objet
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] incertitude géométrique
[Termes IGN] précision de la classificationRésumé : (auteur) Very high spatial resolution multispectral images and lower spatial resolution hyperspectral images are complementary sources for urban object classification. The first enables a fine delineation of objects, while the second can better discriminate classes and consider richer land cover semantics. This paper presents a decision fusion scheme taking advantage of both sources classification maps, to produce a better classification map. The proposed method aims at dealing with both semantic and spatial uncertainties and consists in two steps. First, class membership maps are merged at pixel level. Several fusion rules are considered and compared in this study. Secondly, classification is obtained from a global regularization of a graphical model, involving a fit-to-data term related to class membership measures and an image based contrast sensitive regularization term. Results are presented on three datasets. The classification accuracy is improved up to 5 %, with comparison to the best single source classification accuracy. Numéro de notice : C2017-022 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLII-1-W1-167-2017 Date de publication en ligne : 10/06/2017 En ligne : https://doi.org/10.5194/isprs-archives-XLII-1-W1-167-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89282
Titre : Urban objects classification by spectral library: Feasibility and applications Type de document : Article/Communication Auteurs : Walid Ouerghemmi Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Conférence : JURSE 2017, Joint urban remote sensing event 06/03/2017 08/03/2017 Lausanne Suisse Proceedings IEEE Importance : pp 1 - 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'objet
[Termes IGN] étude de faisabilité
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] milieu urbain
[Termes IGN] reconnaissance d'objetsRésumé : (auteur) Objects recognition in urban environment using multiband imagery is a difficult process, implying the use of elaborated and complex image processing methods, which are used to enhance the detection efficiency. The urban mosaics are characterized by multiple materials (e.g. manmade, urban vegetation, bare soil, transport infrastructure, etc.), which are combined together to form a complex patchwork. This study aims to take advantage of the multiband imagery, to assess the feasibility degree of the urban objects detection, and to explore some of the applications related to the multiband hyperspectral imagery classification. Numéro de notice : C2017-036 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/URBANISME Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2017.7924629 Date de publication en ligne : 11/05/2017 En ligne : https://doi.org/10.1109/JURSE.2017.7924629 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91864
