Unsupervised Dimensionality Reduction for Hyperspectral Imagery via Local Geometric Structure Feature Learning

Abstract

Hyperspectral images (HSIs) possess a large number of spectral bands, which easily lead to the curse of dimensionality. To improve the classification performance, a huge challenge is how to reduce the number of spectral bands and preserve the valuable intrinsic information in the HSI. In this letter, we propose a novel unsupervised dimensionality reduction method called local neighborhood structure preserving embedding (LNSPE) for HSI classification. At first, LNSPE reconstructs each sample with its spectral neighbors and obtains the optimal weights for constructing the adjacency graph by modifying its loss function. Then, to discover the scatter information of the training samples, LNSPE minimizes the scatter between the pixels and the corresponding neighbors and maximizes the total scatter of the HSI data. Finally, it incorporates the scatter information and the dual graph structure to enhance the aggregation of the HSI. As a result, LNSPE can effectively reveal the intrinsic structure and improve the classification performance of the HSI data. The experimental results on two real hyperspectral data sets exhibit the efficiency and superiority of LNSPE to some state-of-the-art methods.