Evaluation of five feature selection methods for remote sensing data

This paper evaluates five potential feature selection methods in the application of remote sensing. The five methods include the sequential forward floating selection, the joint pair approach, band selection based on variance, the principal component transform, and the visual-based selection. Optical-sensor image and synthetic aperture radar image are used for experiments. Several recommendations are made based on this study. For optical-sensor images, the five feature selection methods: sequential forward floating selection, joint pair, band selection, principal component transform, and visual-based selection could have about the same classification accuracy using two to five selected features. The case study has shown that the sequential forward floating selection is the best feature selection method for both optical and synthetic aperture radar feature image selection, followed by the joint pair (for two-feature selection), visual-based selection, band selection, and principal component transform. For band L and band X synthetic aperture radar feature images, entropy, homogeneity, inverse difference moment, and maximum probability, East to West and West to East semivariogram, the local mean value, maximum, and minimum are the best features of the co-occurrence matrix model, semivariogram model, and local statistic model. For Landsat TM images band 7, 4, 5, 3, 1, and 2 are significant feature images. Applying the sequential forward floating selection to select two to five features from the potential features can obtain classification accuracy greater than 90%.