Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Zebin Wu, Shun Ye, Jie Wei, Zhihui Wei, Le Sun, Jianjun Liu
1School of Computer Science and Engineering, Nanjing University of Sci. & Tech., Nanjing 210094, China
International Journal of Distributed Sensor Networks


   author={Wu, Zebin and Ye, Shun and Wei, Jie and Wei, Zhihui and Sun, Le and Liu, Jianjun},



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Hyperspectral imaging sensor becomes increasingly important in multi-sensor 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 sixty times on Telsa C2050, which fully satisfies the real-time processing requirements.
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