Comparison of Supervised Classification Algorithms Using a Hyperspectral Image for Land Use/Land Cover Classification
Author:
Affiliation:
1. Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA
Publisher
MDPI
Link
https://www.mdpi.com/2673-4931/29/1/59/pdf
Reference16 articles.
1. Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping;Petropoulos;Expert Syst. Appl.,2012
2. The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis;Shafri;J. Comput. Sci.,2007
3. Land Use/Land Cover (LULC) Change in Suburb of Central Himalayas: A Study from Chandragiri, Kathmandu;Joshi;J. For. Environ. Sci.,2021
4. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images;Raczko;EuJRS,2017
5. Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia;Deilmai;IOP Conf. Ser. Earth Environ. Sci.,2014
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