  This paper presents a systematic study of the effects of hyperspectral pixel
dimensionality reduction on the pixel classification task. We use five
dimensionality reduction methods -- PCA, KPCA, ICA, AE, and DAE -- to compress
301-dimensional hyperspectral pixels. Compressed pixels are subsequently used
to perform pixel classifications. Pixel classification accuracies together with
compression method, compression rates, and reconstruction errors provide a new
lens to study the suitability of a compression method for the task of pixel
classification. We use three high-resolution hyperspectral image datasets,
representing three common landscape types (i.e. urban, transitional suburban,
and forests) collected by the Remote Sensing and Spatial Ecosystem Modeling
laboratory of the University of Toronto. We found that PCA, KPCA, and ICA post
greater signal reconstruction capability; however, when compression rates are
more than 90\% these methods show lower classification scores. AE and DAE
methods post better classification accuracy at 95\% compression rate, however
their performance drops as compression rate approaches 97\%. Our results
suggest that both the compression method and the compression rate are important
considerations when designing a hyperspectral pixel classification pipeline.
