Richards, JA and Kingsbury, NG (2014) Is there a preferred classifier for operational thematic mapping? IEEE Transactions on Geoscience and Remote Sensing, 52. pp. 2715-2725. ISSN 0196-2892Full text not available from this repository.
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. © 1980-2012 IEEE.
|Uncontrolled Keywords:||Classification maximum likelihood classifier (MLC) neural network support vector machine (SVM) thematic mapping|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 13:57|
|Last Modified:||29 Jul 2015 02:31|