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TitreA robust, cross-validation classification method (RCM) for improved mapping accuracy and confidence metrics
AuteurHarris, J R; Grunsky, E C; He, J; Gorodetzky, D; Brown, N
SourceCanadian Journal of Remote Sensing vol. 38, no. 1, 2012 p. 69-90, https://doi.org/10.5589/m12-013
Année2012
Séries alt.Secteur des sciences de la Terre, Contribution externe 20110011
Documentpublication en série
Lang.anglais
DOIhttps://doi.org/10.5589/m12-013
Mediapapier; en ligne; numérique
Formatspdf
ProvinceNunavut
SNRC66A
Lat/Long OENS-98.0000 -96.0000 65.0000 64.0000
Sujetsméthodes analytiques; méthodes statistiques; techniques de cartographie; divers
ProgrammeBases de données couvrant les trois territoires (la télécartographie prédictive), GEM : La géocartographie de l'énergie et des minéraux
Résumé(disponible en anglais seulement)
A modified approach to existing classification procedures, the Robust Classification Method (RCM), is introduced in this study. This algorithm is based on a randomized and repeated sampling of a training dataset in concert with traditional cross-validation of the classification results. A series of predictions (classified maps) and associated uncertainty maps and statistics are produced. The algorithm and associated outputs are discussed and a case study dealing with the classification of surficial materials in an area in Nunavut, Canada (NTS mapsheet 66A) using RCM is presented. The RCM was especially useful for assessing the effects of spectral and spatial variability in the classification process. Specifically, this method provided a majority classification and variability map and confusion statistics to bracket uncertainty in the classification process with respect to statistical (spectral) variability in the training dataset used to perform the classification as well as identifying areas that show spatial variability in classification.
GEOSCAN ID288548