  As an unsupervised dimensionality reduction method, principal component
analysis (PCA) has been widely considered as an efficient and effective
preprocessing step for hyperspectral image (HSI) processing and analysis tasks.
It takes each band as a whole and globally extracts the most representative
bands. However, different homogeneous regions correspond to different objects,
whose spectral features are diverse. It is obviously inappropriate to carry out
dimensionality reduction through a unified projection for an entire HSI. In
this paper, a simple but very effective superpixelwise PCA approach, called
SuperPCA, is proposed to learn the intrinsic low-dimensional features of HSIs.
In contrast to classical PCA models, SuperPCA has four main properties. (1)
Unlike the traditional PCA method based on a whole image, SuperPCA takes into
account the diversity in different homogeneous regions, that is, different
regions should have different projections. (2) Most of the conventional feature
extraction models cannot directly use the spatial information of HSIs, while
SuperPCA is able to incorporate the spatial context information into the
unsupervised dimensionality reduction by superpixel segmentation. (3) Since the
regions obtained by superpixel segmentation have homogeneity, SuperPCA can
extract potential low-dimensional features even under noise. (4) Although
SuperPCA is an unsupervised method, it can achieve competitive performance when
compared with supervised approaches. The resulting features are discriminative,
compact, and noise resistant, leading to improved HSI classification
performance. Experiments on three public datasets demonstrate that the SuperPCA
model significantly outperforms the conventional PCA based dimensionality
reduction baselines for HSI classification. The Matlab source code is available
at https://github.com/junjun-jiang/SuperPCA
