  Hyperspectral image analysis often requires selecting the most informative
bands instead of processing the whole data without losing the key information.
Existing band reduction (BR) methods have the capability to reveal the
nonlinear properties exhibited in the data but at the expense of loosing its
original representation. To cope with the said issue, an unsupervised
non-linear segmented and non-segmented stacked denoising autoencoder (UDAE)
based BR method is proposed. Our aim is to find an optimal mapping and
construct a lower-dimensional space that has a similar structure to the
original data with least reconstruction error. The proposed method first
confronts the original hyperspectral data into smaller regions in a spatial
domain and then each region is processed by UDAE individually. This results in
reduced complexity and improved efficiency of BR for both semi-supervised and
unsupervised tasks, i.e. classification and clustering. Our experiments on
publicly available hyperspectral datasets with various types of classifiers
demonstrate the effectiveness of UDAE method which equates favorably with other
state-of-the-art dimensionality reduction and BR methods.
