Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs
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 algorith...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/217180 |
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| author | Zebin Wu Shun Ye Jie Wei Zhihui Wei Le Sun Jianjun Liu |
| author_facet | Zebin Wu Shun Ye Jie Wei Zhihui Wei Le Sun Jianjun Liu |
| author_sort | Zebin Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-41ad8f67dd9e430e9b6902d1fbb6d46e |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-41ad8f67dd9e430e9b6902d1fbb6d46e2025-02-03T05:54:32ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-10-01910.1155/2013/217180Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUsZebin Wu0Shun Ye1Jie Wei2Zhihui Wei3Le Sun4Jianjun Liu5 Lianyungang Research Institute of NJUST, Lianyungang 222006, China School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China Jiangsu Key Lab of Spectral Imaging and Intelligent Sensing, Nanjing 210094, China School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaHyperspectral 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.https://doi.org/10.1155/2013/217180 |
| spellingShingle | Zebin Wu Shun Ye Jie Wei Zhihui Wei Le Sun Jianjun Liu Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs International Journal of Distributed Sensor Networks |
| title | Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs |
| title_full | Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs |
| title_fullStr | Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs |
| title_full_unstemmed | Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs |
| title_short | Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs |
| title_sort | fast endmember extraction for massive hyperspectral sensor data on gpus |
| url | https://doi.org/10.1155/2013/217180 |
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