I. Introduction
Because hyperspectral images (HSIs) provide not only spatial information but also spectral information about a scene, they are extensively employed in mineral identification, agriculture yield estimation, and military target detection. Due to the immense number of feature variables (spectral bands), the application of HSIs generally requires a large amount of memory and computation power [1]. As a result, reducing the dimensionality while simultaneously retaining the structural discriminative features is important for HSIs. Dimension reduction can be realized using two ways: feature selection (FS) and feature extraction (FE). FS tends to select the most discriminative features from the existing feature sets, whereas FE aims to find a transform to build the derived informative features. FS can only select from a previously existing feature set while discarding other feature sets, whereas FE can use all features and generate more discriminative features via certain computations. In this letter, we choose to employ FE to reduce the feature dimension in HSIs.