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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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011011B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Land cover classification of cloud-contaminated multitemporal high-resolution images / A. Salberg 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 377 - 387 Titre : Land cover classification of cloud-contaminated multitemporal high-resolution images Type de document : Article/Communication Auteurs : A. Salberg, Auteur Année de publication : 2011 Article en page(s) : pp 377 - 387 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classificateur non paramétrique [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] image multitemporelle [Termes IGN] image optique [Termes IGN] Norvège [Termes IGN] occupation du sol Résumé : (Auteur) We show how methods proposed in the statistical community dealing with missing data may be applied for land cover classification, where optical observations are missing due to clouds and snow. The proposed method is divided into two stages: 1) cloud/snow classification and 2) training and land cover classification. The purpose of the cloud/snow classification stage is to determine which pixels are missing due to clouds and snow. All pixels in each optical image are classified into the classes cloud, snow, water, and vegetation using a suitable classifier. The pixels classified as cloud or snow are labeled as missing, and this information is used in the subsequent training and classification stage, which deals with classification of the pixels into various land cover classes. For land cover classification, we apply the maximum-likelihood (assuming normal distributions), -nearest neighbor, and Parzen classifiers, all modified to handle missing features. The classifiers are evaluated on Landsat (both Thematic Mapper and Enhanced Thematic Mapper Plus) images covering a scene at about 900 m a.s.l. in the Hardangervidda mountain plateau in Southern Norway, where 4869 in situ samples of the land cover classes water, ridge, leeside, snowbed, mire, forest, and rock are obtained. The results show that proper modeling of the missing pixels improves the classification rate by 5%-10%, and by using multiple images, we increase the chance of observing the land cover type substantially. The nonparametric classifiers handle nonignorable missing-data mechanisms and are therefore particularly suitable for remote sensing applications where the pixels covered by snow and cloud may depend on the land cover type. Numéro de notice : A2011-052 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2052464 Date de publication en ligne : 26/07/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2052464 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30833 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011011B	RAB	Revue	Centre de documentation	En réserve L003	Disponible