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A novel MKL model of integrating LiDAR data and MSI for urban area classification / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5312 - 5326 Titre : A novel MKL model of integrating LiDAR data and MSI for urban area classification Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Qingwang Wang, Auteur ; Xiuping Jia, Auteur ; Jón Alti, Auteur Année de publication : 2015 Article en page(s) : pp 5312 - 5326 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] classificateur [Termes IGN] classification à base de connaissances [Termes IGN] classification automatique [Termes IGN] données lidar [Termes IGN] image multibande [Termes IGN] image spectrale [Termes IGN] milieu urbain Résumé : (Auteur) A novel multiple-kernel learning (MKL) model is proposed for urban classification to integrate heterogeneous features (HF-MKL) from two data sources, i.e., spectral images and LiDAR data. The features include spectral, spatial, and elevation attributes of urban objects from the two data sources. With these heterogeneous features (HFs), the new MKL model is designed to carry out feature fusion that is embedded in classification. First, Gaussian kernels with different bandwidths are used to measure the similarity of samples on each feature at different scales. Then, these multiscale kernels with different features are integrated using a linear combination. In the combination, the weights of the kernels with different features are determined by finding a projection based on the maximum variance. This way, the discriminative ability of the HFs is exploited at different scales and is also integrated to generate an optimal combined kernel. Finally, the optimization of the conventional support vector machine with this kernel is performed to construct a more effective classifier. Experiments are conducted on two real data sets, and the experimental results show that the HF-MKL model achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms. Numéro de notice : A2015-752 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421051 Date de publication en ligne : 07/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421051 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78742 [article]

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