Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification

Prime objective of this letter is to select the most relevant features from the original set and perform two steps of feature extraction operations on those selected set, for the classification of hyperspectral remote sensing (HSRS) images. Neighborhood rough sets (NRSs)-based method is used for feature selection because of its excellent neighboring information capturing ability. On these selected features, two steps of extraction operations are performed using the stationary wavelet transform (WT) and stacked deep autoencoder (SDAE). Stationary WT extracts the features by exploiting the spectral–spatial information and stacked DAE extracts through representative learning of input information. The wavelet features and the original input spectral features are cascaded to feed as input to the stacks DAE for feature extraction and classification tasks. The proposed classification model with these operational steps possesses the ability to capture more informative features with improved spectral–spatial information that are highly beneficial for the classification of complex data sets, like HSRS images. Simulation results with two HSRS images justified the efficacy of the proposed model compared to other similar methods in terms of different performance measurement indexes.