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9 October 2018 Hyperspectral compressive sensing: a low-power consumption approach
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Abstract
Hyperspectral imaging instruments allow data collection in hundreds of spectral bands for the same area on the surface of the Earth. The resulting multidimensional data cube typically comprises several GBs per ight. Due to the extremely large volumes of data collected by imaging spectrometers, hyperspectral data compression, dimensionality reduction and Compressive Sensing (CS) techniques has received considerable interest in recent years. These data are usually acquired by a satellite or an airbone instrument and sent to a ground station on Earth for subsequent processing. Usually the bandwidth connection between the satellite/airborne platform and the ground station is reduced, which limits the amount of data that can be transmitted. As a result, there is a clear need for (either lossless or lossy) hyperspectral data compression techniques that can be applied on-board the imaging instrument.

This paper, presents a study of the power and time consumption and accuracy of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform, which is well suited to perform vector operations such as dot products. This implementation exploits the architecture at low level, using shared memory and coalesced accesses to memory. The conducted experiments have been performed to demonstrate the applicability, in terms of accuracy, time consuming and power consumption of these methods for onboard processing. The results show that by using this low power consumption GPU is it possible to obtain real-time performance with a very limited power requirement.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
José M. P. Nascimento, Mário V'estias, and Rui Duarte "Hyperspectral compressive sensing: a low-power consumption approach ", Proc. SPIE 10792, High-Performance Computing in Geoscience and Remote Sensing VIII, 1079202 (9 October 2018); https://doi.org/10.1117/12.2326118
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