Geological Units Classification of Multispectral Images by Using Support Vector Machines
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.
Keywords:
image classification / Landsat / multispectral images / support vector machinesSource:
2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009), 2009, 267-Institution
GraFarTY - CONF AU - Kovačević, Miloš AU - Bajat, Branislav AU - Trivić, Branislav AU - Pavlović, Radmila PY - 2009 UR - http://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 ER -
@conference{ author = "Kovačević, Miloš and Bajat, Branislav and Trivić, Branislav and Pavlović, Radmila", year = "2009", url = "http://grafar.grf.bg.ac.rs/handle/123456789/235", 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" }
Kovačević M, Bajat B, Trivić B, Pavlović R. Geological Units Classification of Multispectral Images by Using Support Vector Machines. 2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009). 2009;:267
Kovačević, M., Bajat, B., Trivić, B.,& Pavlović, R. (2009). Geological Units Classification of Multispectral Images by Using Support Vector Machines. 2009 International Conference On Intelligent Networking and Collaborative Systems (Incos 2009), 267. https://doi.org/10.1109/INCOS.2009.44
Kovačević Miloš, Bajat Branislav, Trivić Branislav, Pavlović Radmila, "Geological Units Classification of Multispectral Images by Using Support Vector Machines" (2009):267, https://doi.org/10.1109/INCOS.2009.44 .