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Morphological feature extraction (MFE) has been successfully used to increase classification accuracy and reduce the noise level for classification or aerial images. In this paper we explore feature selection and extraction for MFE using random forests (RFs) for classification and feature selection. The approach is compared to MFE from principal components extracted from the data, by principal component analysis (PCA), which has been successful in the past. The experimental results presented in this paper show that by estimating the most important features of our data set using RFs, and selecing a few of the features for MFE yields equal or better accuracies than by using PCs.