Algorithms and Prototyping of a Compressive Hyperspectral Imager

Authored by: Alessandro Barducci , Giulio Coluccia , Donatella Guzzi , Cinzia Lastri , Enrico Magli , Valentina Raimondi

Compressive Sensing of Earth Observations

Print publication date:  June  2017
Online publication date:  May  2017

Print ISBN: 9781498774376
eBook ISBN: 9781315154626
Adobe ISBN:

10.1201/9781315154626-15

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Abstract

Compressive sensing (CS) (Candes and Tao 2006) has recently emerged as an efficient technique for sampling a signal with fewer coefficients than dictated by classical Shannon/ Nyquist theory. The assumption underlying this approach is that the signal to be sampled must have concise representation on a convenient basis, meaning that there exists a basis where the signal can be expressed with few large coefficients and many (close-to-)zero coefficients. In CS, sampling is performed by taking a number of linear projections of the signal onto pseudorandom sequences, whereas reconstruction exploits knowledge of a domain where the signal is “sparse.”

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