  This paper introduces the Class-wise Principal Component Analysis, a
supervised feature extraction method for hyperspectral data. Hyperspectral
Imaging (HSI) has appeared in various fields in recent years, including Remote
Sensing. Realizing that information extraction tasks for hyperspectral images
are burdened by data-specific issues, we identify and address two major
problems. Those are the Curse of Dimensionality which occurs due to the
high-volume of the data cube and the class imbalance problem which is common in
hyperspectral datasets. Dimensionality reduction is an essential preprocessing
step to complement a hyperspectral image classification task. Therefore, we
propose a feature extraction algorithm for dimensionality reduction, based on
Principal Component Analysis (PCA). Evaluations are carried out on the Indian
Pines dataset to demonstrate that significant improvements are achieved when
using the reduced data in a classification task.
