  This work proposes a new image analysis tool called Label Consistent
Transform Learning (LCTL). Transform learning is a recent unsupervised
representation learning approach; we add supervision by incorporating a label
consistency constraint. The proposed technique is especially suited for
hyper-spectral image classification problems owing to its ability to learn from
fewer samples. We have compared our proposed method on state-of-the-art
techniques like label consistent KSVD, Stacked Autoencoder, Deep Belief Network
and Convolutional Neural Network. Our method yields considerably better results
(more than 0.1 improvement in Kappa coefficient) than all the aforesaid
techniques.
