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Article

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain
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Remote Sens. 2020, 12(8), 1343; https://doi.org/10.3390/rs12081343
Received: 22 March 2020 / Revised: 16 April 2020 / Accepted: 22 April 2020 / Published: 23 April 2020
(This article belongs to the Section Remote Sensing Image Processing)
The on-board processing of remotely sensed hyperspectral images is gaining momentum for applications that demand a quick response as an alternative to conventional approaches where the acquired images are off-line processed once they have been transmitted to the ground segment. However, the adoption of this on-board processing strategy brings further challenges for the remote-sensing research community due to the high data rate of the new-generation hyperspectral sensors and the limited amount of available on-board computational resources. This situation becomes even more stringent when different time-sensitive applications coexist, since different tasks must be sequentially processed onto the same computing device. In this work, we have dealt with this issue through the definition of a set of core operations that extracts spectral features useful for many hyperspectral analysis techniques, such as unmixing, compression and target/anomaly detection. Accordingly, it permits the concurrent execution of such techniques reusing operations and thereby requiring much less computational resources than if they were separately executed. In particular, in this manuscript we have verified the goodness of our proposal for the concurrent execution of both the lossy compression and anomaly detection processes in hyperspectral images. To evaluate the performance, several images taken by an unmanned aerial vehicle have been used. The obtained results clearly support the benefits of our proposal not only in terms of accuracy but also in terms of computational burden, achieving a reduction of roughly 50% fewer operations to be executed. Future research lines are focused on extending this methodology to other fields such as target detection, classification and dimensionality reduction. View Full-Text
Keywords: hyperspectral imaging; lossy compression; anomaly detection; hardware-friendly; advanced processing algorithms; concurrent computing; push-broom scanners; line-by-line processing; orthogonal projections; real-time applications hyperspectral imaging; lossy compression; anomaly detection; hardware-friendly; advanced processing algorithms; concurrent computing; push-broom scanners; line-by-line processing; orthogonal projections; real-time applications
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MDPI and ACS Style

Díaz, M.; Guerra, R.; Horstrand, P.; López, S.; López, J.F.; Sarmiento, R. Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations. Remote Sens. 2020, 12, 1343. https://doi.org/10.3390/rs12081343

AMA Style

Díaz M, Guerra R, Horstrand P, López S, López JF, Sarmiento R. Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations. Remote Sensing. 2020; 12(8):1343. https://doi.org/10.3390/rs12081343

Chicago/Turabian Style

Díaz, María, Raúl Guerra, Pablo Horstrand, Sebastián López, José F. López, and Roberto Sarmiento. 2020. "Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations" Remote Sensing 12, no. 8: 1343. https://doi.org/10.3390/rs12081343

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