The Tensor Discriminant Ridge Regression Model With Extreme Learning Machine for Hyperspectral Image Classification

Abstract

Multivariate ridge regression (MR), linear discriminant analysis (LDA) and extreme learning machine (ELM) have been widely used in hyperspectral image (HSI) classification. However, these methods do not consider the influence of noise in HSIs, spatial information, and the internal relationship between samples. As a result, the sample distribution is not ideal and the classification effect cannot be improved. This article extends LDA and MR to the field of tensors, that can not only use the spatial information of the sample, but also can make the distribution of homogeneous samples more concentrated. Besides, this article analyzes the relationship between the number of neurons in the hidden layer of ELM and the classification accuracy. Finally, singular spectral analysis (SSA) is chosen to improve classification accuracy. The tensor discriminant ridge regression model with ELM and SSA for HSI classification is proposed. Experiments show compared with tensor-based classifiers, ELM and other state-of-the-art methods, the proposed method is efficient and effective.

Keywords

• Classification
• extreme learning machine (ELM)
• hyperspectral imaging (HSI)
• linear discriminant analysis (LDA)
• singular spectral analysis (SSA)
• tensors