Onboard Payload-Data Dimensionality Reduction
2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)(2017)
摘要
The finer spatial, spectral and radiometric resolutions of current and planned sensors are rendering increasingly high 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 bymultispectral 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.
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关键词
spectral decorrelation, dimensionality reduction, compression, multispectral, hyperspectral, neural network, multi-core, GPU, parallel computing
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