Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images

Abstract

Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. SGE explores the sparsity of the HSI data and can achieve good results. However, in most cases, locality is more important than sparsity when learning the features of the data. In this letter, we propose an extended SGE method: the weighted sparse graph based DR (WSGDR) method for HSIs. WSGDR explicitly encourages the sparse coding to be local and pays more attention to those training pixels that are more similar to the test pixel in representing the test pixel. Furthermore, WSGDR can offer data-adaptive neighborhoods, which results in the proposed method being more robust to noise. The proposed method was tested on two widely used HSI data sets, and the results suggest that WSGDR obtains sparser representation results. Furthermore, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods.