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TitleA comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada's Arctic.
AuthorHe, J; Harris, J R; Sawada, M; Behnia, P
SourceInternational Journal of Remote Sensing vol. 36, issue 8, 2015.,
Alt SeriesEarth Sciences Sector, Contribution Series 20150048
PublisherInforma UK Limited
Mediapaper; on-line; digital
File formatpdf
NTS87E; 87G; 87F; 87H
AreaVictoria Island
Lat/Long WENS-120.0000 -112.0000 72.0000 70.0000
Subjectsremote sensing; LANDSAT imagery; mapping techniques; geological surveys; Neural Network (NN); Support Vector Machine (SVM); Random Forest (RF); Maximum Likelihood Classifier (MLC)
Illustrationssatellite images; location maps; tables; graphs
ProgramGEM: Geo-mapping for Energy and Minerals Geomapping for Energy & Minerals (GEM) - Program Coordination
Released2015 04 23
AbstractTo map Arctic lithology in central Victoria Island, Canada, the relative performance of advanced classifiers (Neural Network (NN), Support Vector Machine (SVM), and Random Forest (RF)) were compared to maximum likelihood classifier (MLC) results using Landsat-7 and Landsat-8 imagery. A ten-repetition cross-validation classification approach was applied. Classification performance was evaluated visually and statistically using the global classification accuracy, producer's and user's accuracy for each individual lithogical/spectral class, and cross-comparison agreement. The advanced classifiers outperformed MLC especially when training data was not normally distributed. The Landsat-8 classification results were comparable to Landsat-7 using the advanced classifiers but differences were more pronounced when using MLC. Re-scaling the Landsat-8 data from 16 bit to 8 bit substantially increased classification accuracy when MLC was applied but had little impact on results from the advanced classifiers.
Summary(Plain Language Summary, not published)
This paper examines four algorithms for producing geological maps in an Arctic environment. The algorithms require input from the geologist in the form of training areas representative of each rock type to be classified.

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