학술논문
The Tensor Discriminant Ridge Regression Model With Extreme Learning Machine for Hyperspectral Image Classification
Document Type
Periodical
Author
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of. 16:8102-8114 2023
Subject
Language
ISSN
1939-1404
2151-1535
2151-1535
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.