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This paper introduces the Class-wise Principal Component Analysis, asupervised feature extraction method for hyperspectral data. HyperspectralImaging (HSI) has appeared in various fields in recent years, including RemoteSensing. Realizing that information extraction tasks for hyperspectral imagesare burdened by data-specific issues, we identify and address two majorproblems. Those are the Curse of Dimensionality which occurs due to thehigh-volume of the data cube and the class imbalance problem which is common inhyperspectral datasets. Dimensionality reduction is an essential preprocessingstep to complement a hyperspectral image classification task. Therefore, wepropose a feature extraction algorithm for dimensionality reduction, based onPrincipal Component Analysis (PCA). Evaluations are carried out on the IndianPines dataset to demonstrate that significant improvements are achieved whenusing the reduced data in a classification task.

Class-Wise Principal Component Analysis for hyperspectral image feature extraction