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Reducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 177 - 186 Titre : Reducing shadow effects on the co-registration of aerial image pairs Type de document : Article/Communication Auteurs : Matthew Plummer, Auteur ; Douglas A. Stow, Auteur ; Emmanuel Storey, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 177 - 186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de données [Termes IGN] correction des ombres [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] effet d'ombre [Termes IGN] enregistrement de données [Termes IGN] image à haute résolution [Termes IGN] image aérienne [Termes IGN] image multitemporelle [Termes IGN] intensité lumineuse [Termes IGN] masque [Termes IGN] Ransac (algorithme) [Termes IGN] SIFT (algorithme) Résumé : (auteur) Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows. Numéro de notice : A2020-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.4.177 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.4.177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94776 [article]