| Title | A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada's Arctic.
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| Author | He, J; Harris, J R; Sawada, M; Behnia, P |
| Source | International Journal of Remote Sensing vol. 36, issue 8, 2015., https://doi.org/10.1080/01431161.2015.1035410 |
| Image |  |
| Year | 2015 |
| Alt Series | Earth Sciences Sector, Contribution Series 20150048 |
| Publisher | Informa UK Limited |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Province | Nunavut |
| NTS | 87E; 87G; 87F; 87H |
| Area | Victoria Island |
| Lat/Long WENS | -120.0000 -112.0000 72.0000 70.0000 |
| Subjects | remote sensing; LANDSAT imagery; mapping techniques; geological surveys; Neural Network (NN); Support Vector Machine (SVM); Random Forest (RF); Maximum Likelihood Classifier (MLC) |
| Illustrations | satellite images; location maps; tables; graphs |
| Program | GEM: Geo-mapping for Energy and Minerals Geomapping for Energy & Minerals (GEM) - Program Coordination |
| Released | 2015 04 23 |
| Abstract | To 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. |
| GEOSCAN ID | 296412 |
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