Published Jun 15, 2017



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David Alberto Boada-supelano, BSc

Héctor Miguel Vargas-Garcia, BSc

Jaime Octavio Albarracín-Ferreira, PhD

Henry Arguello-Fuentes, PhD

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Abstract

Hyperspectral imaging requires handling a large amount of multidimensional spectral information. Hyperspectral image acquisition, processing, and storage are computationally and economically expensive and, in most cases, slow processes. In recent years, optical architectures have been developed for acquisition of spectral information in compressed form by using a small set of measurements coded by a spatial modulator. This article formulates a processing scheme that allows the measurements acquired by such compressive sampling systems to be used to perform spectral detection of targets, by adapting traditional detection algorithms for use in the compressive sampling model, and shows that the performance is comparable with that obtained by detection processes without compression.

Keywords

Compressive sensing, Hyperspectral target detection, Hyperspectral imaging, Sparsity modelimágenes hiperespectrales, muestreo compresivo, detección de objetivos, modelo de escasez

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How to Cite
Boada-supelano, D. A., Vargas-Garcia, H. M., Albarracín-Ferreira, J. O., & Arguello-Fuentes, H. (2017). A sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture. Ingenieria Y Universidad, 21(2), 273–288. https://doi.org/10.11144/Javeriana.iyu21-2.sasi
Section
Electrical and computer engineering