  Herein, we present a system for hyperspectral image segmentation that
utilizes multiple class--based denoising autoencoders which are efficiently
trained. Moreover, we present a novel hyperspectral data augmentation method
for labelled HSI data using linear mixtures of pixels from each class, which
helps the system with edge pixels which are almost always mixed pixels.
Finally, we utilize a deep neural network and morphological hole-filling to
provide robust image classification. Results run on the Salinas dataset verify
the high performance of the proposed algorithm.
