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Proceedings Paper

Compressed hyperspectral sensing
Author(s): Grigorios Tsagkatakis; Panagiotis Tsakalides
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Paper Abstract

Acquisition of high dimensional Hyperspectral Imaging (HSI) data using limited dimensionality imaging sensors has led to restricted capabilities designs that hinder the proliferation of HSI. To overcome this limitation, novel HSI architectures strive to minimize the strict requirements of HSI by introducing computation into the acquisition process. A framework that allows the integration of acquisition with computation is the recently proposed framework of Compressed Sensing (CS). In this work, we propose a novel HSI architecture that exploits the sampling and recovery capabilities of CS to achieve a dramatic reduction in HSI acquisition requirements. In the proposed architecture, signals from multiple spectral bands are multiplexed before getting recorded by the imaging sensor. Reconstruction of the full hyperspectral cube is achieved by exploiting a dictionary of elementary spectral profiles in a unified minimization framework. Simulation results suggest that high quality recovery is possible from a single or a small number of multiplexed frames.

Paper Details

Date Published: 13 March 2015
PDF: 9 pages
Proc. SPIE 9403, Image Sensors and Imaging Systems 2015, 940307 (13 March 2015); doi: 10.1117/12.2083282
Show Author Affiliations
Grigorios Tsagkatakis, Foundation for Research and Technology-Hellas (Greece)
Panagiotis Tsakalides, Foundation for Research and Technology-Hellas (Greece)
Univ. of Crete (Greece)


Published in SPIE Proceedings Vol. 9403:
Image Sensors and Imaging Systems 2015
Ralf Widenhorn; Antoine Dupret, Editor(s)

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