Randomized Principal Component Analysis for Hyperspectral Image Classification

arXiv:2403.09117v1 Announce Type: cross
Abstract: The high-dimensional feature space of the hyperspectral imagery poses major challenges to the processing and analysis of the hyperspectral data sets. In such a case, dimensionality reduction is necessary to decrease the computational complexity. The random projections open up new ways of dimensionality reduction, especially for large data sets. In this paper, the principal component analysis (PCA) and randomized principal component analysis (R-PCA) for the classification of hyperspectral images using support vector machines (SVM) and …

abstract analysis and analysis arxiv case challenges classification complexity computational cs.cv cs.lg data data sets dimensionality eess.iv feature image major processing random space type