Assessing and Improving Methods for the Effective Use of Landsat Imagery for Classification and Change Detection in Remote Canadian Regions

Assessing and Improving Methods for the Effective Use of Landsat Imagery for Classification and Change Detection in Remote Canadian Regions

Canadian remote areas are characterized by a minimal human footprint, restricted accessibility, ubiquitous lichen/snow cover (e.g. Arctic) or continuous forest with water bodies (e.g. Sub-Arctic). Effective mapping of earth surface cover and land cover changes using free medium-resolution Landsat im...

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Bibliographic Details
Main Author: He, Juan Xia
Other Authors: Sawada, Michael
Language:en
Published: Université d'Ottawa / University of Ottawa 2016
Subjects:
image classification
Neural Network
Support Vector Machine
Random Forest
lithology
geostatistical
regression kriging
data fusion
Land cover
change detection
post-classification
threshold-based
PCA
Online Access:http://hdl.handle.net/10393/34221
http://dx.doi.org/10.20381/ruor-5302

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