Hyperspectral classification using stacked autoencoders with deep learning

In this study, stacked autoencoders which are widely utilized in deep learning research are applied to remote sensing domain for hyperspectral classification. High dimensional hyperspectral data is an excellent candidate for deep learning methods. However, there are no works in literature that focuses on such deep learning approaches for hyperspectral imagery. This study aims to fill this gap by utilizing stacked autoencoders. Experiments are conducted on the Pavia University scene. Using stacked autoencoders, intrinsic representations of the data are learned in an unsupervised way. Using labeled data, these representations are fine tuned. Then, using a soft-max activation function, hyperspectral classification is done. Parameter optimization of Stacked Autoencoders (SAE) is done with extensive experiments. Results are competitive with the state-of-the-art techniques.