  We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral
Image Classification in a semi-supervised setting. The Ladder Network
distinguishes itself from other semi-supervised methods by jointly optimizing a
supervised and unsupervised cost. In many settings this has proven to be more
successful than other semi-supervised techniques, such as pretraining using
unlabeled data. We furthermore show that the convolutional Ladder Network
outperforms most of the current techniques used in hyperspectral image
classification and achieves new state-of-the-art performance on the Pavia
University dataset given only 5 labeled data points per class.
