Hyperspectral Data Dimensionality Reduction: A Comparative Study Between PCA and Autoencoder Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00563811" target="_blank" >RIV/60162694:G43__/26:00563811 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-71397-2_20" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-71397-2_20</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-71397-2_20" target="_blank" >10.1007/978-3-031-71397-2_20</a>
Alternative languages
Result language
angličtina
Original language name
Hyperspectral Data Dimensionality Reduction: A Comparative Study Between PCA and Autoencoder Methods
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Modelling and Simulation for Autonomous Systems MESAS
ISBN
978-3-031-71397-2
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
21
Pages from-to
314-334
Publisher name
Springer Nature Switzerland
Place of publication
Cham
Event location
Palermo
Event date
Oct 17, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001419689800020