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TitleA comparison of different remotely sensed data for classifying bedrock types in Canada's Arctic: Application of the Robust Classification Method and Random Forests
AuthorHarris, J R; He, J X; Rainbird, RORCID logo; Behnia, P
SourceGeological Association of Canada-Mineralogical Association of Canada, Joint Annual Meeting, Abstracts Volume vol. 37, 2014 p. 115 Open Access logo Open Access
LinksOnline - En ligne
Alt SeriesEarth Sciences Sector, Contribution Series 20130439
PublisherGeological Association of Canada
MeetingGeological Association of Canada - Mineralogical Association of Canada Joint Annual Meeting; Fredericton; CA; May 21-23, 2014
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; arctic geology; climate, arctic; remote sensing; vegetation
ProgramGEM: Geo-mapping for Energy and Minerals GEM Tri-Territorial Information management & Databases (Remote Predictive Mapping / Mineral Resource Assessment)
Released2014 01 01
AbstractOver 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.
Summary(Plain Language Summary, not published)
This abstract presents results dealing with the classification of various remotely sensed data for lithological mapping in Canada's Arctic (Victoria Island). The results indicate that in a geological environment where rocks are well exposed, classification can contribute signicantly to the geologic mapping process.

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