A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

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 accuracies for each individual lithological/spectral class, and cross-comparison agreement. The advanced classifiers outperformed MLC, especially when training data were 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. Rescaling 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.

Additional information

Funding

This work was supported by the Geological Survey of Canada (GSC) under the Remote Predictive Mapping Project (RPM), part of the Geo-mapping for minerals and energy (GEMS) programme.