Hyperspectral Band Selection Using Unsupervised Non-Linear Deep Auto Encoder to Train External Classifiers
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In order to make hyperspectral image classification compu- tationally tractable, it is often necessary to select the most informative bands instead to process the whole data without losing the geometrical representation of original data. To cope with said issue, an improved un- supervised non-linear deep auto encoder (UDAE) based band selection method is proposed. The proposed UDAE is able to select the most infor- mative bands in such a way that preserve the key information but in the lower dimensions, where the hidden representation is a non-linear trans- formation that maps the original space to a space of lower dimensions. This work emphasizes to analyze what type of information is needed to preserve the hierarchical UDAE representation while selecting a sub- set from original space. Our experiments on publically available hyper- spectral dataset demonstrate the effectiveness of UDAE method, which equates favorably with other state-of-the-art methods.
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