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Change detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 11 (November 2018) . - pp 733 - 741 Titre : Change detection based on stacked generalization system with segmentation constraint Type de document : Article/Communication Auteurs : Kun Tan, Auteur ; Yusha Zhang, Auteur ; Qian Du, Auteur ; Peijun Du, Auteur ; Xiao Jin, Auteur ; Jiayi Li, Auteur Année de publication : 2018 Article en page(s) : pp 733 - 741 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] détection de changement [Termes IGN] image Quickbird [Termes IGN] image ZiYuan-3 [Termes IGN] segmentation d'image Résumé : (Auteur) Change detection based on a multi-classifier ensemble system can take advantage of multiple classifiers to extract change information in remote sensing images. In this paper, an efficient heterogeneous ensemble algorithm, i.e., the stacked generalization (SG) combined with image segmentation, is proposed to construct a simple multi-classifier ensemble system that can offer better detection accuracy with lower computational cost. Due to the rich spatial information in high-spatial-resolution remote sensing images, structure texture (morphological) and statistical texture features are extracted to construct the input data to the ensemble system along with spectral features. In addition, constrained analysis on segmented objects integrates the smaller heterogeneity segmentation map and pixel-wise change map to generate the final change map. The experiments were carried out on two ZY-3 and a QuickBird dataset. The results show that the proposed algorithm can integrate the advantages of both pixel-wise ensemble and object-oriented methods, and effectively improve the accuracy and stability of change detection. Numéro de notice : A2018-485 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.11.733 Date de publication en ligne : 01/11/2018 En ligne : https://doi.org/10.14358/PERS.84.11.733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91210 [article]

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