Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

Hong Huang,Meili Chen,Yule Duan

doi:10.3390/rs11091039

Hong Huang, Meili Chen + Show 1 more

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https://doi.org/10.3390/rs11091039

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Journal: Remote sensing	Publication Date: May 1, 2019
Citations: 16	License type: CC BY 4.0

Affiliation: Chongqing University

Abstract

Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.

Highlights

With spectral sampling from visible to short-wave infrared region, hyperspectral image (HSI) can provide a spatial scene in hundreds of narrow contiguous spectral channels [1,2]
Some experiments were performed on two real HSI datasets to verity the effectiveness of the proposed method, and several state-of-the-art dimensionality reduction (DR) methods were compared with sparse hypergraph embedding (SSRHE) in the experiments
SSRHE utilizes the hypergraph framework to discover the complex multivariate relationships between interclass samples and intraclass samples, and computes two spatial neighborhood scatters to reveal the spatial correlation between each pixels in HSI, which further enhances the discriminating power of low-dimensional features

Summary

Introduction

With spectral sampling from visible to short-wave infrared region, hyperspectral image (HSI) can provide a spatial scene in hundreds of narrow contiguous spectral channels [1,2]. On the basis of GE, some supervised DR methods are designed to exploit the prior knowledge of training samples for improving classification performance, such as marginal Fisher analysis (MFA) [21], local Fisher discriminant analysis (LFDA) [22], and regularized local discriminant embedding (RLDE) [23]. These direct graph-based DR methods only consider the pairwise relationship between data points, while HSI data usually possess complex relationships such as one sample versus multiple samples (different classes) or one class versus multiple samples.

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