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Proceedings Paper

Embedded GPU implementation of anomaly detection for hyperspectral images
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Paper Abstract

Anomaly detection is one of the most important techniques for remotely sensed hyperspectral data interpretation. Developing fast processing techniques for anomaly detection has received considerable attention in recent years, especially in analysis scenarios with real-time constraints. In this paper, we develop an embedded graphics processing units based parallel computation for streaming background statistics anomaly detection algorithm. The streaming background statistics method can simulate real-time anomaly detection, which refer to that the processing can be performed at the same time as the data are collected. The algorithm is implemented on NVIDIA Jetson TK1 development kit. The experiment, conducted with real hyperspectral data, indicate the effectiveness of the proposed implementations. This work shows the embedded GPU gives a promising solution for high-performance with low power consumption hyperspectral image applications.

Paper Details

Date Published: 20 October 2015
PDF: 6 pages
Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 964608 (20 October 2015); doi: 10.1117/12.2195460
Show Author Affiliations
Yuanfeng Wu, Institute of Remote Sensing and Digital Earth (China)
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)
Bin Yang, Institute of Remote Sensing and Digital Earth (China)
Central South Univ. (China)
Zhengchao Chen, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 9646:
High-Performance Computing in Remote Sensing V
Bormin Huang D.D.S.; Sebastián López; Zhensen Wu; Jose M. Nascimento; Boris A. Alpatov; Jordi Portell de Mora, Editor(s)

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