  This work proposes a new framework for deep learning that has been
particularly tailored for hyperspectral image classification. We learn multiple
levels of dictionaries in a robust fashion. The last layer is discriminative
that learns a linear classifier. The training proceeds greedily, at a time a
single level of dictionary is learnt and the coefficients used to train the
next level. The coefficients from the final level are used for classification.
Robustness is incorporated by minimizing the absolute deviations instead of the
more popular Euclidean norm. The inbuilt robustness helps combat mixed noise
(Gaussian and sparse) present in hyperspectral images. Results show that our
proposed techniques outperforms all other deep learning methods Deep Belief
Network (DBN), Stacked Autoencoder (SAE) and Convolutional Neural Network
(CNN). The experiments have been carried out on benchmark hyperspectral imaging
datasets.
