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Optimizing image resolution to maximize the accuracy of hard classification / K.R. Mccloy in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 8 (August 2007)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 8 (August 2007) . - pp 893 - 903 Titre : Optimizing image resolution to maximize the accuracy of hard classification Type de document : Article/Communication Auteurs : K.R. Mccloy, Auteur ; P.K. Bocher, Auteur Année de publication : 2007 Article en page(s) : pp 893 - 903 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] limite de résolution géométrique [Termes IGN] matrice de confusion [Termes IGN] précision de la classification [Termes IGN] théorie des erreurs [Termes IGN] variance Résumé : (Auteur) There are three strategies by which the accuracy of classification can be improved after the imagery that will be used for the classification has been chosen. These are to improve the definition of the class decision surfaces, to maximize the between class distances, and to reduce the within class variances. This paper reports on work done to investigate the relationship between classification accuracy and within class variances, where generally accepted measures of accuracy derived from the Confusion Matrix are used as the indicators of classification accuracy. This paper shows that the within class variances are a function of image resolution, and it provides a mechanism based on the Average Local Variance (ALV) function to find the resolution that will yield the highest relative within field classification accuracy by minimizing the within class variances. Copyright ASPRS Numéro de notice : A2007-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.8.893 En ligne : http://dx.doi.org/10.14358/PERS.73.8.893 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28732 [article]