Weighted sparse graph based dimensionality reduction for hyperspectral images

Tools

He, Wei and Zhang, Hongyan and Zhang, Liangpei and Philips, Wilfried and Liao, Wenzhi (2016) Weighted sparse graph based dimensionality reduction for hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 13 (5). pp. 686-690. ISSN 1545-598X (https://doi.org/10.1109/LGRS.2016.2536658)

[thumbnail of He-etal-IGRMS2016-Weighted-sparse-graph-based-dimensionality-reduction-hyperspectral-images]
Preview
 Text. Filename: He_etal_IGRMS2016_Weighted_sparse_graph_based_dimensionality_reduction_hyperspectral_images.pdf
Accepted Author Manuscript
Download (605kB)| Preview

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. In this letter, we propose a weighted sparse graph based DR (WSGDR) method for HSIs. Instead of only exploring the locality structure (as in neighborhood preserving embedding) or the linearity structure (as in SGE) of the HSI data, the proposed method couples the locality and linearity properties of HSI data together in a unified framework for the DR of HSIs. The proposed method was tested on two widely used HSI data sets, and the results suggest that the locality and linearity are complementary properties for HSIs. In addition, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods.

CORE (COnnecting REpositories)