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The high-dimensional feature vectors of hyper spectral data often impose a high computational cost as well as the risk of
"over fitting" when classification is performed. Therefore it is necessary to reduce the dimensionality through ways like
feature selection. Currently, there are two kinds of feature selection methods: filter methods and wrapper methods. The
former kind requires no feedback from classifiers and estimates the classification performance indirectly. The latter kind
evaluates the "goodness" of selected feature subset directly based on the classification accuracy. Many experimental
results have proved that the wrapper methods can yield better performance, although they have the disadvantage of high
computational cost. In this paper, we present a Genetic Algorithm (GA) based wrapper method for classification of hyper
spectral data using Support Vector Machine (SVM), a state-of-art classifier that has found success in a variety of areas.
The genetic algorithm (GA), which seeks to solve optimization problems using the methods of evolution, specifically
survival of the fittest, was used to optimize both the feature subset, i.e. band subset, of hyper spectral data and SVM
kernel parameters simultaneously. A special strategy was adopted to reduce computation cost caused by the high-dimensional
feature vectors of hyper spectral data when the feature subset part of chromosome was designed. The GA-SVM
method was realized using the ENVI/IDL language, and was then tested by applying to a HYPERION hyper
spectral image. Comparison of the optimized results and the un-optimized results showed that the GA-SVM method
could significantly reduce the computation cost while improving the classification accuracy. The number of bands used
for classification was reduced from 198 to 13, while the classification accuracy increased from 88.81% to 92.51%. The
optimized values of the two SVM kernel parameters were 95.0297 and 0.2021, respectively, which were different from
the default values as used in the ENVI software. In conclusion, the proposed wrapper feature selection method GA-SVM
can optimize feature subsets and SVM kernel parameters at the same time, therefore can be applied in feature selection
of the hyper spectral data.
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