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Auteur Jinxia Zhu |
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Unsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
[article]
Titre : Unsupervised object-based differencing for land-cover change detection Type de document : Article/Communication Auteurs : Jinxia Zhu, Auteur ; Yanjun Su, Auteur ; Qinghua Guo, Auteur ; Thomas C. Harmon, Auteur Année de publication : 2017 Article en page(s) : pp 225 - 236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme espérance-maximisation
[Termes IGN] altération
[Termes IGN] autocorrélation
[Termes IGN] changement d'occupation du sol
[Termes IGN] Chine
[Termes IGN] classification non dirigée
[Termes IGN] classification orientée objet
[Termes IGN] détection de changement
[Termes IGN] image multitemporelle
[Termes IGN] image SPOT-HRV
[Termes IGN] occupation du sol
[Termes IGN] traitement d'imageRésumé : (Auteur) One main problem of the spectral decomposition-based change detection method is the lack of efficient automatic techniques for developing the difference image. Traditional techniques generally assume that gray-level values in a difference image are independent and multitemporal images are co-registered/rectified perfectly without error. However, such assumptions are often violated because of the inevitable image misregistration and the interference of correlations between spectral bands. This study proposes an automated method based on the object-based multivariate alteration detection/maximum autocorrelation factor approach and the Gaussian mixture model-expectation maximization algorithm to obtain unsupervised difference images. This procedure is applied to bi-temporal (2005 and 2006) SPOT-HRV images at Panyu District Ponds, China. Results show that the proposed method successfully excludes the correlations of spectral bands and the influence of misregistration, as evidenced by a higher accuracy (up to 93.6 percent). These unique technical characteristics make this analytical framework suitable for detecting changes. Numéro de notice : A2017-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.225 En ligne : https://doi.org/10.14358/PERS.83.3.225 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84424
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 225 - 236[article]Reducing mis-registration and shadow effects on change detection in wetlands / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 4 (April 2011)
[article]
Titre : Reducing mis-registration and shadow effects on change detection in wetlands Type de document : Article/Communication Auteurs : Jinxia Zhu, Auteur ; Q. Guo, Auteur ; D. Li, Auteur ; T. Harmon, Auteur Année de publication : 2011 Article en page(s) : pp 325 - 334 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] axe de prise de vue
[Termes IGN] classification orientée objet
[Termes IGN] correction des ombres
[Termes IGN] détection de changement
[Termes IGN] marais
[Termes IGN] ombre
[Termes IGN] seuillage d'image
[Termes IGN] superposition d'imagesRésumé : (Auteur) With respect to the inevitable mis-registration and shadow effects on change detection analysis, we propose object-based post-classification of the Multivariate Alteration Detection components (ob-mad). Very high spatial resolution images of drained, managed wetland ponds were used to compare the proposed OB-MAD method with three commonly used classification methods in terms of minimizing the influence of mis-registration and shadow on the change detection analysis: (a) the traditional mad method with thresholds (Threshold-MAD), (b) a pixel-based post-classification of mad components with decision tree analysis (PB-MAD), and (c) a traditional object-based post-classification method (OB-traditional). The OB-MAD method, which utilizes shape and textural information of objects derived from MAD components, produced the highest accuracy with respect to wetland change detection and successfully minimized the influence from the geometric distortion and shadow on the changed area. Numéro de notice : A2011-127 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.4.325 En ligne : https://doi.org/10.14358/PERS.77.4.325 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30906
in Photogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 4 (April 2011) . - pp 325 - 334[article]