@article {40276,
	title = {Feature selection for classification of polar regions using a fuzzy expert system},
	journal = {Remote Sensing of Environment},
	volume = {58},
	year = {1996},
	month = {1996},
	pages = {81 - 100},
	abstract = {Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the use of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow lice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce tile features into a more optimal set. For each selection method, features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set of features, not only does it produce the highest classification accuracy, but also it has tile lowest computation requirements. A reduction of die set of features produced by the divergence method using tile fuzzy expert system results in an overall classification accuracy of over 95 \%. However, this increase of accuracy has a high computation cost.},
	keywords = {avhrr, classification, features, networks, neural, surface, textural},
	author = {Penaloza,M. A. and Welch,R. M.}
}
