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Band selection consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors. Automatic feature selection (FS) algorithms are used to achieve this selection. It aims at selecting an optimal band subset according to a criterion. This study focuses on a comparison...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) associated to a classifier (linear SVM) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. The impact of the number of selected bands on classification accuracy was obtained thanks to SFFS, while a band importance measure...
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