The finer spatial, spectral and radiometric resolutions of current and planned sensors are rendering increasinglyhigh data rates which, coupled with limited on-board storage, downlink bandwidth and receiving ground station availability, make high-throughput, high-performance data-reduction techniques essential in forthcoming missions. On this paper we describe an algorithm well suited to high-dimensional data as those produced by multispectral and hyperspectral sensors, both highly relevant in a broad range of Earth Observation activities with the latter becoming increasingly available and delivering the highest data rates. The performance of parallel implementations of the algorithm on multi-core and GPU architectures is also evaluated.
PENALVER NIETO, M., DEL FRATE, F., Paoletti, M.e., Haut, J.m., Plaza, J., Plaza, A. (2017). Onboard Payload-Data Dimensionality Reduction. In Proceedings of International Geoscience and Remote Sensing Symposium. IEEE [10.1109/IGARSS.2017.8127069].
Onboard Payload-Data Dimensionality Reduction
Miguel Penalver;Fabio Del Frate;
2017-01-01
Abstract
The finer spatial, spectral and radiometric resolutions of current and planned sensors are rendering increasinglyhigh data rates which, coupled with limited on-board storage, downlink bandwidth and receiving ground station availability, make high-throughput, high-performance data-reduction techniques essential in forthcoming missions. On this paper we describe an algorithm well suited to high-dimensional data as those produced by multispectral and hyperspectral sensors, both highly relevant in a broad range of Earth Observation activities with the latter becoming increasingly available and delivering the highest data rates. The performance of parallel implementations of the algorithm on multi-core and GPU architectures is also evaluated.File | Dimensione | Formato | |
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