Manifold Learning for Hyperspectral Images

Fethi Harkat; Guillaume Gey; Valérie Perrier; Kévin Polisano; Tiphaine Deuberet

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arXiv · 2503.15016

Manifold Learning for Hyperspectral Images

Fethi Harkat·Guillaume Gey·Valérie Perrier·Kévin Polisano·Tiphaine Deuberet

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Abstract

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.

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Fethi Harkat, Guillaume Gey, Valérie Perrier, Kévin Polisano, Tiphaine Deuberet. 2025-03-19. Manifold Learning for Hyperspectral Images. https://arxiv.org/abs/2503.15016

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