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Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery / Komeil Rokni in Geodesy and cartography, vol 49 n° 1 (January 2023)  

Public [article]  inGeodesy and cartography > vol 49 n° 1 (January 2023) . - pp 12 - 18 Titre : Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery Type de document : Article/Communication Auteurs : Komeil Rokni, Auteur Année de publication : 2023 Article en page(s) : pp 12 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de Gram-Schmidt [Termes IGN] carte d'occupation du sol [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] Kappa de Cohen [Termes IGN] matrice de confusion [Termes IGN] pansharpening (fusion d'images) [Termes IGN] précision de la classification Résumé : (auteur) Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose. Numéro de notice : A2023-142 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3846/gac.2023.15308 Date de publication en ligne : 17/02/2023 En ligne : https://doi.org/10.3846/gac.2023.15308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102712 [article]