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A PCA-PD fusion method for change detection in remote sensing multi temporal images / Soltana Achour in Geocarto international, vol 37 n° 1 ([01/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 1 [01/01/2022] . - pp 196 - 213 Titre : A PCA-PD fusion method for change detection in remote sensing multi temporal images Type de document : Article/Communication Auteurs : Soltana Achour, Auteur ; Miloud Chikr Elmezouar, Auteur ; Nasreddine Taleb, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 196 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] fusion de données [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image panchromatique [Termes IGN] méthode statistique [Termes IGN] seuillage d'image Résumé : (auteur) In remote sensing, for applications as environment monitoring, change detection based on image processing is one of the most important techniques. To reach high performance various techniques of fusion are exploited using a combination of multi-temporal, multispectral and panchromatic satellite images. A solution for handling such kind of images holds when using some simple statistical methods like the Percent Difference (PD) technique as well as the Principal Component Analysis (PCA) one. In this paper, an automatic change detection method issued from the two previous techniques is proposed and applied on multispectral and panchromatic images captured by a high resolution optical satellite. This approach is characterized by two aspects: the first one consists of the fusion of the different data and the second one performs the detection of the changes for the resulting images. The experimental results show the reasonable quantitative performance and the effectiveness of the proposed method for change detection, consisting of an automatic extraction of most of change information as well as the obtention of better results for most precision metrics consisting of an overall accuracy of up to 91% and a Kappa coefficient of up to 66%, comparing to those obtained using the simple PD and PCA techniques. Numéro de notice : A2022-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1713228 Date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1080/10106049.2020.1713228 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99441 [article]

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