Semisupervised Dimensionality Reduction of Hyperspectral Images via Local Scaling Cut Criterion

Abstract

Hyperspectral images (HSIs) provide a vast amount of geometrical, radiation, and spectral information about a scene. However, high-dimensional data make HSI classification complex and time consuming. It is important to reduce the dimensionality and find a low-dimensional representation of the high-dimensional data. Since the labels of HSI data are really difficult to collect while the unlabeled data are abundant and easy to obtain, in this letter, a semisupervised dimensionality reduction method using both limited labeled samples and a large number of unlabeled samples based on a local scaling cut (LSC) criterion is proposed. LSC is similar to linear discriminant analysis (LDA), but it can handle the heteroscedastic and multimodal data for which LDA fails. The framework of our proposed method contains two terms: 1) a discrimination term based on the labeled samples and 2) a regularization term based on the prior knowledge provided by both labeled and unlabeled samples. Experimental results show that our proposed algorithm provides a relatively promising performance compared with other methods. Moreover, the algorithm is stable and insensitive to parameters.