Quantitative techniques for spatial prediction and classification in geological survey are developing rapidly. The recent applications of machine learning techniques confirm possibilities of their application in this field of research. The paper introduces Support Vector Machines, a method derived from recent achievements in the statistical learning theory, in classification of geological units based on the source of the Landsat multispectral images. The initial experiments suggest the usefulness of the proposed classification approach.
TY - CONF
AU - Kovačević, Miloš
AU - Bajat, Branislav
AU - Trivić, Branislav
AU - Pavlović, Radmila
PY - 2009
UR - https://grafar.grf.bg.ac.rs/handle/123456789/235
AB - Quantitative techniques for spatial prediction and classification in geological survey are developing rapidly. The recent applications of machine learning techniques confirm possibilities of their application in this field of research. The paper introduces Support Vector Machines, a method derived from recent achievements in the statistical learning theory, in classification of geological units based on the source of the Landsat multispectral images. The initial experiments suggest the usefulness of the proposed classification approach.
C3 - 2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009)
T1 - Geological Units Classification of Multispectral Images by Using Support Vector Machines
SP - 267
DO - 10.1109/INCOS.2009.44
UR - conv_1542
ER -
@conference{
author = "Kovačević, Miloš and Bajat, Branislav and Trivić, Branislav and Pavlović, Radmila",
year = "2009",
abstract = "Quantitative techniques for spatial prediction and classification in geological survey are developing rapidly. The recent applications of machine learning techniques confirm possibilities of their application in this field of research. The paper introduces Support Vector Machines, a method derived from recent achievements in the statistical learning theory, in classification of geological units based on the source of the Landsat multispectral images. The initial experiments suggest the usefulness of the proposed classification approach.",
journal = "2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009)",
title = "Geological Units Classification of Multispectral Images by Using Support Vector Machines",
pages = "267",
doi = "10.1109/INCOS.2009.44",
url = "conv_1542"
}
Kovačević, M., Bajat, B., Trivić, B.,& Pavlović, R.. (2009). Geological Units Classification of Multispectral Images by Using Support Vector Machines. in 2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009), 267.
https://doi.org/10.1109/INCOS.2009.44
conv_1542
Kovačević M, Bajat B, Trivić B, Pavlović R. Geological Units Classification of Multispectral Images by Using Support Vector Machines. in 2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009). 2009;:267.
doi:10.1109/INCOS.2009.44
conv_1542 .
Kovačević, Miloš, Bajat, Branislav, Trivić, Branislav, Pavlović, Radmila, "Geological Units Classification of Multispectral Images by Using Support Vector Machines" in 2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009) (2009):267,
https://doi.org/10.1109/INCOS.2009.44 .,
conv_1542 .
