Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image

Luo, Fulin and Guo, Tan and Lin, Zhiping and Ren, Jinchang and Zhou, Xiaocheng (2020) Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13. pp. 4242-4256. ISSN 1939-1404 (

[thumbnail of Luo-etal-IEEE-JSTAEORS-2020-Semisupervised-hypergraph-discriminant-learning-for-dimensionality-reduction]
Text. Filename: Luo_etal_IEEE_JSTAEORS_2020_Semisupervised_hypergraph_discriminant_learning_for_dimensionality_reduction.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (18MB)| Preview


Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. In this article, we introduce the hypergraph into semisupervised learning to reveal the complex multistructures of an HSI, and construct a semisupervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The result indicates that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of an HSI.