Manifold Learning for Hyperspectral Images
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912715739496448 |
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| author | Harkat, Fethi Gey, Guillaume Perrier, Valérie Polisano, Kévin Deuberet, Tiphaine |
| author_facet | Harkat, Fethi Gey, Guillaume Perrier, Valérie Polisano, Kévin Deuberet, Tiphaine |
| contents | Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images, limiting the performance of neural networks in decision-making processes. To address this issue, we propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection. This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms, particularly in processing Hyperspectral Images (HSI) from X-ray transmission spectroscopy. This technique not only preserves the global structure of the data but also enhances feature separability, leading to more accurate and robust classification results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15016 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Manifold Learning for Hyperspectral Images Harkat, Fethi Gey, Guillaume Perrier, Valérie Polisano, Kévin Deuberet, Tiphaine Computer Vision and Pattern Recognition Machine Learning Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images, limiting the performance of neural networks in decision-making processes. To address this issue, we propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection. This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms, particularly in processing Hyperspectral Images (HSI) from X-ray transmission spectroscopy. This technique not only preserves the global structure of the data but also enhances feature separability, leading to more accurate and robust classification results. |
| title | Manifold Learning for Hyperspectral Images |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.15016 |