| 基于GPU的高光谱遥感图像PPI并行优化 |
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| 引用本文： | 宋义刚,叶舜,吴泽彬,韦志辉.基于GPU的高光谱遥感图像PPI并行优化[J].航天返回与遥感,2014(4):74-80. |
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| 作者姓名： | 宋义刚 叶舜 吴泽彬 韦志辉 |
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| 作者单位： | 1. 南京理工大学计算机科学与工程学院,南京,210094
2. 南京理工大学计算机科学与工程学院，南京 210094; 南京理工大学连云港研究院，连云港 222006 |
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| 基金项目： | CAST创新基金项目(CAST201227)；国家自然科学基金(61101194)；江苏省自然科学基金(BK2011701)；中国地质调查局工作项目(1212011120227)；江苏省“六大人才高峰”项目(WLW-011)；高等学校博士学科点专项科研基金资助项目 |
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| 摘 要： | 传统高光谱遥感信息处理算法的执行效率较低,无法满足海量遥感数据的实时处理需求。文章对基于图形处理器（graphic processing unit,GPU）的高光谱遥感信息处理并行优化方法进行了研究,针对高光谱遥感图像混合像元分解中广泛使用的纯净像元指数算法,提出了一种基于矩阵乘法的GPU并行优化算法,并给出了实验比较和性能测试数据。实验表明,该优化方法在保证结果准确性的同时,运行效率显著提升,算法加速比最高达到634倍,验证了基于GPU的高光谱数据处理并行优化算法的有效性,能够较好地满足高光谱遥感信息实时处理的应用需求。
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| 关 键 词： | 高光谱遥感 并行优化 纯净像元指数 图形处理器 |
Parallel Optimization of Pixel Purity Index Algorithm Based on GPU for Hyperspectral Remote Sensing Image |
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| SONG Yigang,YE Shun,WU Zebin,WEI Zhihui.Parallel Optimization of Pixel Purity Index Algorithm Based on GPU for Hyperspectral Remote Sensing Image[J].Spacecraft Recovery & Remote Sensing,2014(4):74-80. |
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| Authors: | SONG Yigang YE Shun WU Zebin WEI Zhihui |
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| Institution: | SONG Yigang, YE Shun, WU Zebin,WEI Zhihui ( 1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China ;2. Lianyungang Research Institute, Nanjing University of Science and Technology, Lianyungang 222006, China ) |
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| Abstract: | Low efficiency of traditional hyperspectral processing algorithm can not meet the demand of mass remote sensing data real-time processing. This paper exploits the paralleled optimization method based on graphic processing unit （GPU） for hyperspectral image processing, and develops a parallel optimized algorithm based on the matrix multiplication. The algorithm is designed for pixel purity index （PPI）, which is widely applied in mixed pixel decomposition. In the end, it gives a detailed experimental comparison and performance testing. The experiments show that GPU-based parallel optimization methods significantly enhance the efficiency of the implementation while maintaining the accuracy of the results. This new parallel algorithm acceleration values reaches up to 634 times, which demonstrates the effectiveness of the GPU-based parallel optimization algorithm and promotes the real-time information processing of hyperspectral remote sensing. |
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| Keywords: | hyperspectral remote sensing parallel optimization pixel purity index graphic processing unit |
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