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13 March 2015 Compressed hyperspectral sensing
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Proceedings Volume 9403, Image Sensors and Imaging Systems 2015; 940307 (2015) https://doi.org/10.1117/12.2083282
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
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.
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Grigorios Tsagkatakis and Panagiotis Tsakalides "Compressed hyperspectral sensing", Proc. SPIE 9403, Image Sensors and Imaging Systems 2015, 940307 (13 March 2015); https://doi.org/10.1117/12.2083282
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