Dimensionality Reduction for Hyperspectral Data Based on Pairwise Constraint Discriminative Analysis and Nonnegative Sparse Divergence

Abstract

Listen

Translate article

To improve the classification accuracy of unlabeled large-scale hyperspectral data, a dimensionality reduction algorithm based on pairwise constraint discriminant analysis and nonnegative sparse divergence (PCDA-NSD) is proposed by using the feature transfer learning technology. Different from labeled sample information that is relatively difficult to acquire, pairwise constraints are a kind of useful supervision information, which can be automatically acquired without artificial interference and thus can better avoid the selection of redundant and noisy samples. Therefore, the pairwise constraint discriminant analysis method is used to learn potential discriminant information of sample sets in the source and target domains. Consequently, positively correlated constraint samples in the source and target domains share one subspace whereas positively and negatively correlated constraint samples are highly separated. Because hyperspectral data in the source and target domains often follow different distributions, a nonnegative sparse divergence is established to measure the divergence between different distributions, based on the nonnegative sparse representation method. Therefore, not only the computation load of the kernel matrix is reduced, but also the natural discriminant capacity is obtained. Experiments of a four-group hyperspectral data show that PCDA-NSD can reduce dimensionality of target data and improve classification accuracy and efficiency by adequate use of the information available in similar hyperspectral data.