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Title: Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations
Authors: Díaz Martín, María 
Guerra Hernández, Raúl Celestino 
Horstrand Andaluz, Pablo Sebastian 
López Suárez, Sebastián 
López Feliciano, José Francisco 
Sarmiento Rodríguez, Roberto 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Hyperspectral imaging
Lossy compression
Anomaly detection
Advanced processing algorithms, et al
Issue Date: 2020
Project: European Initiative to Enable Validation for Highly Automated Safe and Secure Systems 
Plataforma H2/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturales 
Journal: Remote Sensing 
Abstract: 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.
ISSN: 2072-4292
DOI: 10.3390/rs12081343
Source: Remote Sensing [ISSN 2072-4292], v. 12 (8), 1343, (Abril 2020)
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