基于异构模式的高光谱数据加速处理方法研究

GPU-based accelerated processing of atmospheric hyperspectral remote sensing data

随着遥感卫星更高时空分辨率的大气探测需求,大气遥感高光谱数据量骤增,传统高光谱数据的处理效率较低,无法满足高性能处理高光谱数据的需求.首先介绍了国内外研究者利用图形处理器(GPU)加速处理遥感高光谱数据的应用实例,然后对基于CPU-GPU异构模式的大气遥感高光谱数据傅里叶分析的并行化计算进行了研究,并进行算法实现,最后同传统基于CPU计算做了实验比较.实验结果表明,使用基于CPU-GPU异构模式的方法处理高光谱数据时,在保证数据准确性的同时速度提升80多倍,验证了将GPU加速用于处理大气遥感高光谱数据的有效性,可以更好地满足气象卫星更高时空分辨率的大气探测需求.

The demand for higher temporal and spatial resolution of atmospheric detection has brought about rapid increase of atmospheric hyperspectral remote sensing data;however, traditional methods have low efficiency for hyperspectral data processing.Here, we summarize the application examples of using GPU to accelerate the processing of hyperspectral remote sensing data, among which we focus on the parallel computing of the Fourier analysis of hyperspectral data based on the CPU-GPU heterogeneous mode.Then the CPU-GPU heterogeneous mode based parallel computing is implemented and then compared with traditional CPU-based computing.The results show that the data processing speed is increased by about 90 times while ensuring the data accuracy as well, thus verify the effectiveness of GPU to accelerate the processing of atmospheric remote sensing hyperspectral data.