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Graph-based feature selection for object-oriented classification in VHR airborne imagery / Tianen Chen in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 1 Tome 2 (January 2011) . - pp 353 - 365 Titre : Graph-based feature selection for object-oriented classification in VHR airborne imagery Type de document : Article/Communication Auteurs : Tianen Chen, Auteur ; Tian Fang, Auteur ; H. Huo, Auteur ; D. Li, Auteur Année de publication : 2011 Article en page(s) : pp 353 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification dirigée [Termes IGN] classification orientée objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] graphe [Termes IGN] image à ultra haute résolution [Termes IGN] image aérienne [Termes IGN] Kappa de Cohen [Termes IGN] matrice [Termes IGN] pondération [Termes IGN] similitude [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) Linearly nonseparability and class imbalance of very high resolution (VHR) imagery make feature selection for object-oriented classification quite challenging, while such characteristics, especially class imbalance, have usually been ignored in open literature. To cope with the challenges, this paper proposes a new graph-based feature selection method named locally weighted discriminating projection (LWDP). First, the popular graph-based criteria of feature selection are reformulated to present linear or nonlinear mapping in feature space. Second, weight matrices of graphs characterize dissimilarity rather than similarity between pairwise neighbors, to well-preserved local structure when the difference of distance between a sample and its neighbors is large. Finally, LWDP provides a new perspective to alleviate class imbalance at both global and local levels, by restricting the pairwise relationships in the weight matrices. Specifically, neighborhood unions are introduced to employ the local class distribution and class size to constrain pairwise relationships in the weight matrices when classifying unbalanced sample sets. To evaluate the performances of LWDP in low dimensions, a holistic scoring scheme is proposed to stress the performances under low dimensions. In addition, overall accuracy curves and Kappa Index of Agreement (KIA) curves, which exhibit KIA in dimensions, are also used. The experimental results show that LWDP and its kernel extension outperform the other classic or latest methods in processing unbalanced sample set of VHR airborne imagery. Numéro de notice : A2011-051 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2054832 Date de publication en ligne : 12/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2054832 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30832 [article]

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