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Is There a Preferred Classifier for Operational Thematic Mapping?
- Source :
- IEEE Transactions on Geoscience and Remote Sensing. 52:2715-2725
- Publication Year :
- 2014
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2014.
-
Abstract
- The importance of properly exploiting a classifier's inherent geometric characteristics when developing a classification methodology is emphasized as a prerequisite to achieving near optimal performance when carrying out thematic mapping. When used properly, it is argued that the long-standing maximum likelihood approach and the more recent support vector machine can perform comparably. Both contain the flexibility to segment the spectral domain in such a manner as to match inherent class separations in the data, as do most reasonable classifiers. The choice of which classifier to use in practice is determined largely by preference and related considerations, such as ease of training, multiclass capabilities, and classification cost.
- Subjects :
- Structured support vector machine
Contextual image classification
business.industry
Computer science
Linear classifier
Image segmentation
Quadratic classifier
computer.software_genre
Machine learning
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Margin classifier
General Earth and Planetary Sciences
Artificial intelligence
Data mining
Electrical and Electronic Engineering
business
computer
Classifier (UML)
Subjects
Details
- ISSN :
- 15580644 and 01962892
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Geoscience and Remote Sensing
- Accession number :
- edsair.doi...........800e3f97e5e5d77064d8d66fe0bac3ed