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Measuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery / X. Chen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 4837 - 4851 Titre : Measuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery Type de document : Article/Communication Auteurs : X. Chen, Auteur ; Tao Fang, Auteur ; Hong Huo, Auteur ; Deren Li, Auteur Année de publication : 2015 Article en page(s) : pp 4837 - 4851 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données thématiques [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à très haute résolution [Termes IGN] image satellite Résumé : (Auteur) Generally, some object-based features are more relevant to a thematic class than other features. These strongly relevant features, termed as class-specific features, would significantly contribute to thematic information extraction for very high resolution (VHR) images. However, many existing feature selection methods have been designed to select a good feature subset for all classes, rather than an independent feature subset for the thematic class. The latter might better meet the requirement of thematic information extraction than the former. In addition, the lack of quantitative evaluation of the contribution of the selected features to thematic classes also weakens our understandability of these features. To address the problems, class-specific feature selection methods are developed to measure the effectiveness of features for extracting thematic information from VHR images. First, the one-versus-all scheme is combined with traditional feature selection methods, such as ReliefF and LeastC. Also, one-versus-one scheme is utilized for alleviating the negative impact of a class imbalance problem arising from the one-versus-all scheme. Then, the relative contributions of features to thematic classes are obtained by the class-specific feature selection methods to describe the effectiveness of features for thematic information extraction. Finally, the class-specific feature selection methods are compared with the original methods on three different VHR image data sets by the nearest neighbor and support vector machine. Experimental results show that the class-specific feature selection methods outperform the corresponding conventional methods, and the one-versus-one scheme surpasses one-versus-all scheme. Additionally, many features are evaluated by the class-specific feature selection methods, to provide end users advice on effectiveness of the features. Numéro de notice : A2015-529 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2411331 Date de publication en ligne : 27/03/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2411331 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77557 [article]

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065-2015091	SL	Revue	Centre de documentation	Revues en salle	Disponible

Bi-temporal texton forest for land cover transition detection on remotely sensed imagery / Zhen Lei in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 1227 - 1237 Titre : Bi-temporal texton forest for land cover transition detection on remotely sensed imagery Type de document : Article/Communication Auteurs : Zhen Lei, Auteur ; Tao Fang, Auteur ; Hong Huo, Auteur ; Deren Li, Auteur Année de publication : 2014 Article en page(s) : pp 1227 - 1237 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] arbre de décision [Termes IGN] détection de changement [Termes IGN] gradient [Termes IGN] occupation du sol Résumé : (Auteur) With the advancement of machine learning, classification methods have been increasingly used in change (or transition) detection. The texton forest (TF)-based method has received increasing research attention because of its speed, good generalization characteristics, stability, and especially its ability to capture spatial contextual information. In this paper, we propose a TF-based method for transition detection in remotely sensed imagery. We investigate a maximal joint-information gain criterion for random forests to better capture combined information in the bi-temporal images in transition detection, which is implemented by a natural extension of binary-trees in traditional methods into a quad-decision tree structure. We also utilize color-invariant gradient as a feature to help alleviate the impact of difference in imaging conditions on bi-temporal transition detection. The experimental results for transition detection show that our bi-temporal TF classifier achieves better performance than a post-classification comparison method and several other alternative methods. Numéro de notice : A2014-075 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2248738 En ligne : https://doi.org/10.1109/TGRS.2013.2248738 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32980 [article]

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065-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible