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TitreA comparison of different remotely sensed data for classifying bedrock types in Canada's Arctic: Application of the Robust Classification Method and Random Forests
AuteurHarris, J R; He, J X; Rainbird, R; Behnia, P
SourceL'Association géologique du Canada-L'Association minéralogique du Canada, Réunion annuelle conjointe, Recueil des résumés vol. 37, 2014 p. 115
LiensOnline - En ligne
Année2014
Séries alt.Secteur des sciences de la Terre, Contribution externe 20130439
ÉditeurAssociation géologique du Canada
RéunionGeological Association of Canada - Mineralogical Association of Canada Joint Annual Meeting; Fredericton; CA; mai 21-23, 2014
Documentpublication en série
Lang.anglais
Mediapapier; en ligne; numérique
Formatspdf
Sujetsgéologie de l'arctique; climat arctique; télédétection; végétation; géophysique
ProgrammeBases de données couvrant les trois territoires (la télécartographie prédictive), GEM : La géocartographie de l'énergie et des minéraux
Résumé(disponible en anglais seulement)
Over the past three decades, the increasing availability of space - borne sensors imaging the Earth's surface using increasingly higher spatial and spectral resolutions has evolved geologic remote sensing from being primarily a qualitative discipline to a quantitative discipline based on the computer analysis of digital images. The Geological Survey of Canada under the Remote Predictive Mapping (RPM) project part of the Geo - mapping for Energy and Minerals (GEM) program, Natural Resources Canada, has the mandate to produce up - to - date geoscience maps of Canada's territory north of 600. Classification of remotely sensed data is a well - known and common image processing application that has been used since the early 1970's concomitant with the launch of the first LANDSAT (ERTS) earth observational satellite. In this study we apply supervised classification using a new algorithm known as the Robust Classification Method (RCM) and a Random Forest (RF) classifier to a variety of remotely sensed data including LAND SAT - 7, LANDSAT - 8, SPOT - , ASTER and airborne magnetic imagery producing predictions (classifications) of primarily bedrock lithology and quaternary cover in central Victoria Island, Northwest Territories. We compare and contrast these different data types and evaluate how well they classify various lithologies and surficial materials using confusion analysis (confusion matrices) as well as comparing the generalized classifications with the newly produced geology map of the study area. In addition we propose some new ensemble classification methods that leverage the best characteristics of all remotely sensed data used for classification. Both RCM and RF provide good classification results. However, RF provides the highest classification accuracy because it used all 43 of the raw and derived bands from all the remotely sensed data. The ensemble classifications based on the generalized training dataset showed the best agreement with the new geology map for the study area.
Résumé(Résumé en langage clair et simple, non publié)
Ce résumé présente les résultats de la classification de différentes données de la télédétection, à des fins de cartographie lithologique de l'Arctique canadien (l'île Victoria). Les résultats indiquent que dans un environnement géologique où les roches sont bien exposées, la classification peut contribuer considérablement au processus de cartographie géologique.
GEOSCAN ID293581