10.1109/WHISPERS.2010.5594951">
 

Document Type

Conference Proceeding

Publication Date

6-2010

Abstract

Feature subset selection is a well studied problem in machine learning. One short-coming of many methods is the selection of highly correlated features; a characteristic of hyperspectral data. A novel stochastic feature selection method with three major components is presented. First, we present an optimized feature selection method that maximizes a heuristic using a simulated annealing search which increases the chance of avoiding locally optimum solutions. Second, we exploit local cross correlation pair-wise amongst classes of interest to select suitable features for class discrimination. Third, we adopt the concept of distributed spacing from the multi-objective optimization community to distribute features across the spectrum in order to select less correlated features. The classification performance of our semi-embedded feature selection and classification method is demonstrated on a 12-class textile hyperspectral classification problem under several noise realizations. These results are compared with a variety of feature selection methods that cover a broad range of approaches. Abstract © IEEE

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AFIT Scholar furnishes the accepted version of this conference paper. The published version of record is available from IEEE via subscription at the DOI link in the citation below.

Source Publication

Second Workshop on Hyperspectral Image and Signal processing: Evolution in Remote Sensing (IEEE WHISPERS), 2010, pp. 1-4.

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