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International Journal of Distributed Sensor Networks
Volume 2013 (2013), Article ID 217180, 7 pages
Research Article

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2Lianyungang Research Institute of NJUST, Lianyungang 222006, China
3Jiangsu Key Lab of Spectral Imaging and Intelligent Sensing, Nanjing 210094, China

Received 14 July 2013; Accepted 12 September 2013

Academic Editor: Zhijie Han

Copyright © 2013 Zebin Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hyperspectral imaging sensor becomes increasingly important in multisensor collaborative observation. The spectral mixture problem seriously influences the efficiency of hyperspectral data exploitation, and endmember extraction is one of the key issues. Due to the high computational cost of algorithm and massive quantity of the hyperspectral sensor data, high-performance computing is extremely demanded for those scenarios requiring real-time response. A method of parallel optimization for the well-known N-FINDR algorithm on graphics processing units (NFINDR-GPU) is proposed to realize fast endmember extraction for massive hyperspectral sensor data in this paper. The implements of the proposed method are described and evaluated using compute unified device architecture (CUDA) based on NVIDA Quadra 600 and Telsa C2050. Experimental results show the effectiveness of NFINDR-GPU. The parallel algorithm is stable for different image sizes, and the average speedup is over thirty times on Telsa C2050, which satisfies the real-time processing requirements.