GEOSCAN Search Results: Fastlink


TitleRandom forest modelling and classification of Landsat ETM+ and DEM data for surficial material mapping
AuthorParkinson, W; Richardson, M; Russell, HORCID logo
Source33rd Canadian symposium on remote sensing, abstracts; by Canadian Remote Sensing Society; 2012 p. 40 Open Access logo Open Access
LinksOnline - En ligne
LinksAbstracts (PDF, 1.22 MB)
Alt SeriesEarth Sciences Sector, Contribution Series 20120280
Meeting33rd Canadian symposium on remote sensing; Ottawa; CA; June 11-14, 2012
Mediaon-line; digital
File formatpdf
Lat/Long WENS-112.0000 -110.0000 64.0000 63.0000
Subjectsmiscellaneous; remote sensing; modelling
ProgramGEM: Geo-mapping for Energy and Minerals GEM Tri-Territorial Information management & Databases (Remote Predictive Mapping / Mineral Resource Assessment)
AbstractThere is a need at the Geological Survey of Canada to apply improved accuracy assessments of satellite image classification and support remote predictive mapping techniques for geological map production and field operations. Most existing classification algorithms, however, lack any robust capabilities for assessing image classification accuracy and its variability throughout the landscape. In this study, random forest classification was used to improve overall image classification accuracy and to better describe its spatial variability across a heterogeneous landscape in Northern Canada.

Random forest models are stochastic implementations of classification and regression trees, which are computationally efficient and effectively handle outlier bias in predictor variables. This algorithm can also be used on non-parametric data sources, which is practical for surficial materials when similar materials types can manifest on a wide variety of spectral scales. The importance of individual variables is quantitatively assessed with respect to its ability to explain variance within the response variable. Prediction assessment metrics can be generated in order to identify areas of uncertainty in the classification. Random forest provides an enhanced classification compared to the standard maximum likelihood algorithms and greatly improves predictive capacity of satellite imagery.

Case study results from NTS map sheet 75M (northeast of Great Slave Lake) is presented for a synoptic geological material interpretation. The study area was classified with accuracy assessment diagnostics included as part of the classification product. Adoption of this methodology could improve field mapping planning, focus validation, and identify specific areas of interest in the landscape.

Date modified: