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An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine
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DOI：10.1016/j.knosys.2010.07.003.png)
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Abstract
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With the development and popularization of the remote-sensing imaging technology, there are more and more applications of hyperspectral image classification tasks, such as target detection and land cover investigation. It is a very challenging issue of urgent importance to select a minimal and effective subset from those mass of bands. This paper proposed a hybrid feature selection strategy based on genetic algorithm and support vector machine (GA-SVM), which formed a wrapper to search for the best combination of bands with higher classification accuracy. In addition, band grouping based on conditional mutual information between adjacent bands was utilized to counter for the high correlation between the bands and further reduced the computational cost of the genetic algorithm. During the post-processing phase, the branch and bound algorithm was employed to filter out those irrelevant band groups. Experimental results on two benchmark data sets have shown that the proposed approach is very competitive and effective. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Hyperspectral remote sensing
Band selection
Conditional mutual information
Support vector machine
Genetic algorithm
Branch and bound algorithm
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