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CSSNET: A Learning Algorithm for the Segmentation of Compressed Hyperspectral Images

Maud Biquard 1 Simon Lacroix 2 Antoine Rouxel 1 Hervé Carfantan 3 Antoine Monmayrant 1 Henri Camon 1 
1 LAAS-PHOTO - Équipe Photonique
LAAS - Laboratoire d'analyse et d'architecture des systèmes
2 LAAS-RIS - Équipe Robotique et InteractionS
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : The paper presents a semantic segmentation method which is directly applicable to compressed hyperspectral images acquired with a dual-disperser CASSI instrument. It introduses an algorithm based on a shallow neural network that exploits the spectral filtering performed by the optical system and the compressed hyperspectral images measured by the detector. Encouraging results that exploit 50 to 100 less data than the whole hyperspectral datacube on PaviaU and IndianPines datasets are presented.
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https://hal.laas.fr/hal-03746594
Contributor : Simon Lacroix Connect in order to contact the contributor
Submitted on : Friday, August 5, 2022 - 3:49:07 PM
Last modification on : Thursday, August 18, 2022 - 3:49:00 PM

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whispers2022.pdf
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  • HAL Id : hal-03746594, version 1

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Maud Biquard, Simon Lacroix, Antoine Rouxel, Hervé Carfantan, Antoine Monmayrant, et al.. CSSNET: A Learning Algorithm for the Segmentation of Compressed Hyperspectral Images. Workshop on Hyperspectral Images and Signal Processing: Evolution in Remote Sensing (WHISPERS 2022), Sep 2022, Rome, Italy. ⟨hal-03746594⟩

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