Hyperspectral Image Data Classification with Refined Spectral Spatial Features Based on Stacked Autoencoder Approach

Background: Hyperspectral (HS) image data comprises of tremendous amount of spatial and spectral information which offers feature identification and classification with high accuracy. As part of the Deep Learning (DL) framework Stacked Autoencoders (SAEs) has been successfully applied for deep spectral features extraction in high dimensional data. HS deep image feature extraction becomes complex and time consuming due to the hundreds of spectral bands available in the hypercubes. Methods: The proposed method aims condense the spectral-spatial information through suitable feature extraction and feature selection methods to reduce data dimension to an appropriate scale. Further, the reduced feature set is processed by SAE for final feature representation and classification. Results: The proposed method has resulted in reduced computation time by ∼ 300s and an improvement in classification accuracy by ~15% as compared to uncondensed spectral-spatial features fed directly to SAE network. Conclusion: Future research could explore the combination of most state-of-the art techniques.