Abstract
Hyperspectral imaging has emerged as a powerful tool for remote sensing providing detection and identification of objects of interest using their unique spectral signature. Accurate information obtained can reveal details about the physical properties of materials that are relevant to intelligence gathering. Those interesting characteristics coupled with Unmanned Aerial Vehicles (UAVs) offer the perspective of easier detection of camouflaged army gear on the battlefield. This paper presents some aspect of hyperspectral measurements and processing and focuses on workload reduction and latent data representation using Principal Component Analysis (PCA) and autoencoder techniques. Both techniques are compared through a simple segmentation method that shows their efficiency in reducing the dimensionality of the hyperspectral data, spectrum-wise and image-wise.
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Acknowledgment
The work presented in this article has been supported by the Ministry of Defence and the University of Defence of the Czech Republic through the development program of the organization “VAROPS – DZRO military autonomous and robotic systems”.
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Motsch, J., Bergeon, Y., Křivánek, V. (2025). Hyperspectral Data Dimensionality Reduction: A Comparative Study Between PCA and Autoencoder Methods. In: Mazal, J., et al. Modelling and Simulation for Autonomous Systems. MESAS 2023. Lecture Notes in Computer Science, vol 14615. Springer, Cham. https://doi.org/10.1007/978-3-031-71397-2_20
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