| Contributors: |
Raimondi, Valentina; Acampora, Luigi; Amato, Gabriele; Baldi, Massimo; Berndt, Dirk; Bianchi, Alberto; Bianchi, Tiziano; Colcelli, Valentina; Corti, Chiara; Corti, Francesco; Corti, Marco; Cox, Nick; Dauderstädt, Ulrike A.; Dürr, Peter; Francés González, Sara; Frosini, Paolo; Guzzi, Donatella; Huntingford, Jessica; Labate, Demetrio; Lamquin, Nicola; Lastri, Cinzia; Magli, Enrico; Nardino, Vanni; Palombi, Lorenzo; Pettinelli, Irene; Pilato, Giuseppe; Pollini, Alexandre; Rossini, Leopoldo; Suetta, Enrico; Taricco, Davide; Valsesia, Diego; Wagner, Michael |
| Description: |
While Earth Observation (EO) data has become ever more vital to understanding the planet and addressing societal challenges, applications are still limited by revisit time and spatial resolution. Though low Earth orbit missions can achieve resolutions better than 100 m, their revisit time typically stands at several days, limiting capacity to monitor dynamic events. Geostationary (GEO) missions instead typically provide data on an hour-basis but with spatial resolution limited to 1 km, which is insufficient to understand local phenomena. In this paper, we present the SURPRISE project - recently funded in the frame of the H2020 programme – that gathers the expertise from eight partners across Europe to implement a demonstrator of a super-spectral EO payload - working in the visible (VIS) - Near Infrared (NIR) and in the Medium InfraRed (MIR) and conceived to operate from GEO platform -with enhanced performance in terms of at-ground spatial resolution, and featuring innovative on-board data processing and encryption functionalities. This goal will be achieved by using Compressive Sensing (CS) technology implemented via Spatial Light Modulators (SLM). SLM-based CS technology will be used to devise a super-resolution configuration that will be exploited to increase the at-ground spatial resolution of the payload, without increasing the number of detector’s sensing elements at the image plane. The CS approach will offer further advantages for handling large amounts of data, as is the case of superspectral payloads with wide spectral and spatial coverage. It will enable fast on-board processing of acquired data for information extraction, as well as native data encryption on top of native compression. SURPRISE develops two disruptive technologies: Compressive Sensing (CS) and Spatial Light Modulator (SLM). CS optimises data acquisition (e.g. reduced storage and transmission bandwidth requirements) and enables novel onboard processing and encryption functionalities. SLM here implements the CS paradigm and achieves a ... |