Wavelet feature extraction and genetic feature selection for multisource data

A linear feature extraction method based on the discrete wavelet transform (DWT) is applied. A binary genetic algorithm is used to select the best features from the different DWT representations in terms of cost. The feature extraction/selection methods are applied in classification of multisource remote sensing and geographic data. In experiments, the proposed methods performed well in terms of overall accuracies as compared to results obtained with other well-known feature extraction/selection methods.