|Title||Analysis of Landsat ETM+ Image enhancement for lithological classification improvement in Eagle Plain area, northern Yukon|
|Author||Zhao, S; Guest, B; Lane, L S|
|Source||Proceedings of GeoConvention 2014 FOCUS; 2014 p. 1-6|
|Links||Online - En ligne|
|Alt Series||Earth Sciences Sector, Contribution Series 20140068|
|Publisher||Canadian Society of Petroleum Geologists|
|Meeting||GeoConvention 2014 FOCUS; Calgary; CA; May 12-16, 2014|
|NTS||116G/14; 116G/15; 116G/16|
|Lat/Long WENS||-139.5000 -138.0000 66.0000 65.7500|
|Subjects||geophysics; satellite imagery; remote sensing; lithology; Landsat|
|Illustrations||location maps; histograms; flow charts|
|Program||Yukon Sedimentary Basins, GEM:
Geo-mapping for Energy and Minerals|
Remote sensing image enhancement techniques including principal component analysis, band ratioing and grey level co-occurrence matrix have been employed to improve the lithological
classification using Landsat ETM+ data in the present study. A principal component analysis (PCA) has been performed on the six Landsat ETM+ spectral bands to extract the pertinent information from the different bands and isolate the noise in its own
band or bands which can then be ignored. Grey level co-occurrence matrix (GLCM) is one of the widely used approaches to perform textural feature measurment. A variety of measures have extracted useful textural information from co-occurrence matrices.
Band ratioing was performed on six Landsat ETM+ spectral bands to reduce the effects of environmental factors such as cast shadows in mountainous terrain with high relief. A supervised classification was performed individually on the six original
multispectral ETM+ bands and six new datasets of combination of principal components, ratioing bands and GLCM-based textural parameters. The classification accuracy assessment based on the field investigation and existing geological map shows that
the integration of principal component analysis, band ratioing and texture feature analysis had a great contribution in improving the lithological classification in the study area using Landsat ETM+ data.
|Summary||(Plain Language Summary, not published)|
This abstract summarizes University of Calgary funded MSc research by the first author, supervised by the second and third authors. The presentation
summarizes the first author's investigation into various image analysis techniques that can be applied to satellite imagery in order to enhance the accuracy of rock type and rock unit interpretations. This will improve geological maps by increasing
the reliability of rock unit assignments extrapolated from limited field work, which is helpful in very remote or inaccessible areas.