Embedded GPU implementation of anomaly detection for hyperspectral images

Yuanfeng Wu; Lianru Gao; Bing Zhang; Bin Yang; Zhengchao Chen

doi:10.1117/12.2195460

20 October 2015 Embedded GPU implementation of anomaly detection for hyperspectral images

Yuanfeng Wu, Lianru Gao, Bing Zhang, Bin Yang, Zhengchao Chen

Proceedings Volume 9646, High-Performance Computing in Remote Sensing V; 964608 (2015) https://doi.org/10.1117/12.2195460
Event: SPIE Remote Sensing, 2015, Toulouse, France

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.

Citation Download Citation

Yuanfeng Wu, Lianru Gao, Bing Zhang, Bin Yang, and Zhengchao Chen "Embedded GPU implementation of anomaly detection for hyperspectral images", Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 964608 (20 October 2015); https://doi.org/10.1117/12.2195460

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