  This paper presents a systematic study the effects of compression on
hyperspectral 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-based 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-based classification. We use three high-resolution hyperspectral image
datasets, representing three common landscape units (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
rate is more than 90\% those methods showed lower classification scores. AE and
DAE methods post better classification accuracy at 95\% compression rate,
however decreasing again at 97\%, suggesting a sweet-spot at the 95\% mark. Our
results demonstrate that the choice of a compression method with the
compression rate are important considerations when designing a hyperspectral
image classification pipeline.
