I. Introduction
A hyperspectral image (HSI) contains dozens or even hundreds of contiguous spectral bands covering the electromagnetic spectrum from visible to near-infrared regions [1], [2]. Such images are captured by hyperspectral sensors [3]. In recent years, HSIs have been widely used in the fields of environmental monitoring, precision agriculture, object recognition, and land cover classification [4]–[6]. Despite their high-dimensional characteristic, HSI data are typically redundant with underlying structures that can be represented by only a few features [7], [8]. Therefore, the main challenge for HSI classification is to reduce the dimensionality of data points and remove redundancy. Applying feature reduction, including feature selection (FS) and feature extraction (FE), is an effective way to overcome this challenge and preserve relevant information in data points [9], [10]. FS involves directly choosing the best subbands from all spectral bands on the basis of certain criteria, whereas FE can be used to map high-dimensional data into a low-dimensional feature space with a number of FE algorithms. In this paper, we only focus on the FE method.