Academic Journal
The Tensor Discriminant Ridge Regression Model With Extreme Learning Machine for Hyperspectral Image Classification
| العنوان: | The Tensor Discriminant Ridge Regression Model With Extreme Learning Machine for Hyperspectral Image Classification |
|---|---|
| المؤلفون: | Xinpeng Wang, Bingo Wing-Kuen Ling, Huimin Zhao, Shaopeng Liu |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 8102-8114 (2023) |
| بيانات النشر: | IEEE, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Classification, extreme learning machine (ELM), hyperspectral imaging (HSI), linear discriminant analysis (LDA), singular spectral analysis (SSA), tensors, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10227511/; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2023.3308031 |
| URL الوصول: | https://doaj.org/article/dd09ee7b0d8e412aa32d8d8af03e9918 |
| رقم الانضمام: | edsdoj.09ee7b0d8e412aa32d8d8af03e9918 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 21511535 |
|---|---|
| DOI: | 10.1109/JSTARS.2023.3308031 |