10570

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
@article{wu2013fast,

   title={FAST ENDMEMBER EXTRACTION FOR MASSIVE HYPERSPECTRAL SENSOR DATA ON GPUS},

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

   year={2013}

}

Download Download (PDF)   View View   Source Source   

401

views

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.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1496 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

255 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: