The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study. (arXiv:2104.00788v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2104.00788

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 …

arxiv case study classification cv pixel study