Chapter

A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification

DOI: 10.1007/978-3-642-12990-2_37

ABSTRACT With the development of the remote-sensing imaging technology, there are more and more applications of hyperspectral image
classification tasks, in which to select a minimal and effective subset from a mass of bands is the key issue. This paper
put forward a novel band selection strategy based on conditional mutual information between adjacent bands and branch and
bound algorithm for the high correlation between the bands. In addition, genetic algorithm and support vector machine are
employed to search for the best band combination. Experimental results on two benchmark data set have shown that this approach
is competitive and robust.

KeywordsHyperspectral Remote Sensing-Band Selection-Conditional Mutual Information-Support Vector Machine-Genetic Algorithm-Branch and Bound Algorithm

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    ABSTRACT: 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.
    Knowledge-Based Systems 02/2011; 24(1-24):40-48. DOI:10.1016/j.knosys.2010.07.003 · 3.06 Impact Factor