Centre de documentation
scientifique

Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data / Moussa Sofiane Karoui in Remote sensing, vol 11 n° 18 (September 2019)  

Public [article]  inRemote sensing > vol 11 n° 18 (September 2019) . - n° 2164 Titre : Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data Type de document : Article/Communication Auteurs : Moussa Sofiane Karoui, Auteur ; Fatima Zohra Benhalouche, Auteur ; Yannick Deville, Auteur ; Khelifa Djerriri, Auteur ; Xavier Briottet , Auteur ; Thomas Houet, Auteur ; Arnaud Le Bris , Auteur ; Christiane Weber, Auteur Année de publication : 2019 Projets : HYEP / Weber, Christiane Article en page(s) : n° 2164 Note générale : bibliographie This paper constitutes a substantial extension of: https://doi.org/10.1109/IGARSS.2018.8518204 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] détection d'objet [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] panneau photovoltaïque [Termes IGN] zone urbaine Résumé : (auteur) High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature. Numéro de notice : A2019-430 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11182164 Date de publication en ligne : 17/09/2019 En ligne : https://doi.org/10.3390/rs11182164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93739 [article]

Detection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original NMF-based unmixing method / Moussa Sofiane Karoui (2018)  

Public Titre : Detection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original NMF-based unmixing method Type de document : Article/Communication Auteurs : Moussa Sofiane Karoui, Auteur ; Fatima Zohra Benhalouche, Auteur ; Yannick Deville, Auteur ; Khelifa Djerriri, Auteur ; Xavier Briottet , Auteur ; Arnaud Le Bris , Auteur 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 1640 - 1643 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse des mélanges spectraux [Termes IGN] détection de contours [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] panneau photovoltaïque [Termes IGN] zone urbaine Résumé : (auteur) Hyperspectral remote sensing data offer unique opportunities for the characterization of land surface in urban areas. However, no hyperspectral-unmixing based studies have been conducted to automatically detect photovoltaic panels, which represent one of the important components of energy systems in such areas. In this paper, a hyperspectral-unmixing based method is proposed to detect photovoltaic panels and to estimate their areas. This approach is based on an original multiplicative nonnegative matrix factorization (NMF) algorithm with some known photovoltaic panel spectra. The proposed method can be considered as a partial/informed NMF approach. Experiments are conducted on realistic synthetic and real data to evaluate the performance of the proposed approach. In both cases, obtained results show that the proposed method yields much better overall performance than a method from the literature. Numéro de notice : C2018-047 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8518204 Date de publication en ligne : 05/11/2018 En ligne : https://doi.org/10.1109/IGARSS.2018.8518204 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91270