HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)
The classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data. As such, we offer a unique hyperspectral image classification methodology to address these issues based on sophistica...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-02-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W6-2022/179/2023/isprs-archives-XLVIII-4-W6-2022-179-2023.pdf |
| _version_ | 1811169301004025856 |
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| author | A. Jamali M. Mahdianpari M. Mahdianpari A. Abdul Rahman |
| author_facet | A. Jamali M. Mahdianpari M. Mahdianpari A. Abdul Rahman |
| author_sort | A. Jamali |
| collection | DOAJ |
| description | The classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data. As such, we offer a unique hyperspectral image classification methodology to address these issues based on sophisticated Multi-Layer Perceptron (MLP) algorithms. In this paper, we propose using MLP-Mixer to classify HSI data in three data benchmarks of Pavia, Salinas, and Indian Pines. Based on the results, the proposed MLP-Mixer achieved a high level of classification accuracy and produced noise-free and homogenous classification maps in all study areas. For the classification of HSI data in Salinas, Indian Pines, and Pavia, the proposed MLP-Mixer achieved an average accuracy of 99.82%, 99.81%, and 99.23%, respectively. |
| first_indexed | 2024-04-10T16:40:08Z |
| format | Article |
| id | doaj.art-842d5afe58f149b0a9100f34f168f3a4 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-10T16:40:08Z |
| publishDate | 2023-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-842d5afe58f149b0a9100f34f168f3a42023-02-08T07:51:45ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-02-01XLVIII-4-W6-202217918210.5194/isprs-archives-XLVIII-4-W6-2022-179-2023HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)A. Jamali0M. Mahdianpari1M. Mahdianpari2A. Abdul Rahman3Faculty of Engineering, University of Karabük, Karabük, TürkiyeDepartment of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B3X5, CanadaC-CORE, 1 Morrissey Rd, St. John’s, NL A1B 3X5, CanadaFaculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, MalaysiaThe classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data. As such, we offer a unique hyperspectral image classification methodology to address these issues based on sophisticated Multi-Layer Perceptron (MLP) algorithms. In this paper, we propose using MLP-Mixer to classify HSI data in three data benchmarks of Pavia, Salinas, and Indian Pines. Based on the results, the proposed MLP-Mixer achieved a high level of classification accuracy and produced noise-free and homogenous classification maps in all study areas. For the classification of HSI data in Salinas, Indian Pines, and Pavia, the proposed MLP-Mixer achieved an average accuracy of 99.82%, 99.81%, and 99.23%, respectively.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W6-2022/179/2023/isprs-archives-XLVIII-4-W6-2022-179-2023.pdf |
| spellingShingle | A. Jamali M. Mahdianpari M. Mahdianpari A. Abdul Rahman HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) |
| title_full | HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) |
| title_fullStr | HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) |
| title_full_unstemmed | HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) |
| title_short | HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) |
| title_sort | hyperspectral image classification using multi layer perceptron mixer mlp mixer |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W6-2022/179/2023/isprs-archives-XLVIII-4-W6-2022-179-2023.pdf |
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