Optical Compressive Imaging Technologies for Space Big Data
| Autor: | Cinzia Lastri, Raffaele Vitulli, Giulio Coluccia, Daniela Coltuc, Ivan Pippi, Chiara Ravazzi, Alessandro Zuccaro Marchi, Enrico Magli, Valentina Raimondi, Donatella Guzzi, Vanni Nardino, Lorenzo Palombi, Florin Garoi |
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| Rok vydání: | 2020 |
| Předmět: |
Big Data
Earth observation Information Systems and Management Computer science Space Applications Big data Compressed Sensing Big Data Space Applications Compressive Imaging Hyperspectral Imaging Spatial Light Modulators Detectors Earth Observation Space Science Planetary Exploration Aerospace electronics 02 engineering and technology Iterative reconstruction Encryption 01 natural sciences Imaging Compressed Sensing Space Science 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 010303 astronomy & astrophysics Signal processing Planetary Exploration Sensors business.industry Image coding Hyperspectral imaging Detectors 020206 networking & telecommunications Hyperspectral Imaging Compressive Imaging Computer engineering Image reconstruction Key (cryptography) Earth Observation Instruments business Spatial Light Modulators Information Systems |
| Zdroj: | IEEE Transactions on Big Data (2019). doi:10.1109/TBDATA.2019.2907135 info:cnr-pdr/source/autori:Giulio Coluccia, Cinzia Lastri, Donatella Guzzi, Enrico Magli, Vanni Nardino, Lorenzo Palombi, Ivan Pippi, Valentina Raimondi, Chiara Ravazzi, Florin Garoi, Daniela Coltuc, Raffaele Vitulli, Alessandro Zuccaro Marchi/titolo:Optical Compressive Imaging Technologies for Space Big Data/doi:10.1109%2FTBDATA.2019.2907135/rivista:IEEE Transactions on Big Data/anno:2019/pagina_da:/pagina_a:/intervallo_pagine:/volume |
| ISSN: | 2372-2096 |
| DOI: | 10.1109/tbdata.2019.2907135 |
| Popis: | The increasing amount of data generated by space applications poses several challenges due to limited resources available onboard: power, memory, computation, data rate. In this paper, we propose Compressed Sensing (CS) as the key tool to face those challenges via compressive imaging. This signal processing technique, only recently applied to space applications, dramatically simplifies the image acquisition featuring native compression/encryption and enabling onboard image analysis, allowing to design simpler and lighter optical systems. In this paper, we try to answer the following question: To what extent are the potential benefits of CS going to materialize in a realistic “space big data” application scenario? To this purpose, we first review compressive imaging techniques and already existing prototypes and concepts, critically discussing the technological issues involved. Then, we propose a set of instrument concepts in the application domains of space science, planetary exploration and earth observation, most suitable for a CS–based application. For the most promising of them, we go deeper into the analysis showing preliminary reconstruction performance tests. |
| Databáze: | OpenAIRE |
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