Unchaining hyperspectral imaging with quantum-inspired compression (UHIQIC)

Clausen, Christoph (Dotphoton AG, Zug, Switzerland) ; Sanguinetto, Bruno (Dotphoton AG, Zug, Switzerland) ; Akhtman, Yosef (ALTYN, Geneva, Switzerland) ; Pomarico, Enrico (School of Engineering, Architecture and Landscape (hepia), HES-SO // University of Applied Sciences Western Switzerland) ; Extermann, Jérôme (School of Engineering, Architecture and Landscape (hepia), HES-SO // University of Applied Sciences Western Switzerland)

With hyperspectral imaging, image content can be identified based on fine spectral details related to chemical composition. Immediate applications in smart agriculture and environmental monitoring have the potential for strong societal benefits. However, the technology struggles with the vast amount of data that it produces, in particular when deployed on satellites. The current movement towards increased use of lossy compression is highly risky, because even careful and tedious parameter tuning cannot guarantee that no applications are compromised. We implemented and validated a compression method that simultaneously provides a strong data reduction and preserves analysis results for all possible applications.


Keywords:
Conference Type:
published full paper
Faculty:
Ingénierie et Architecture
School:
HEPIA - Genève
Institute:
inSTI - Institut des Sciences et Technologies industrielles
Publisher:
Online, 22 September 2020
Date:
2020-09
Online
22 September 2020
Pagination:
5 p.
Published in:
ATTRACT Online Conference "Igniting the Deep Tech Revolution", 22 September 2020, Online
Appears in Collection:



 Record created 2021-05-04, last modified 2021-05-07

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