Design and Optimization of Remote Sensing Image Fusion Parallel Algorithms based on CPU-GPU Heterogeneous Platforms

摘要：

Remote sensing image fusion emerges as an important approach to improve the utilization of information from multi-source remote sensing image. As the resolution of time, space and spectrum continuously increases, the remote sensing image data becomes extremely large. In this paper, we propose a novel parallel processing model to exploit its data parallelism on the heterogeneous CPU-GPU platform. While taking advantage of NVIDIAS CUDA (Compute Unified Device Architecture) programming technology, we apply this model to the YIQ transform fusion algorithm and IHS transform with wavelet enhancement fusion algorithm. We have optimized and implemented these paraUel algorithms on NVIDIA GTX460 GPU. Experimental results show that the proposed parallel model has an outstanding performance and scalability. The maximum speedup is up to 114X compared with the serial CPU program. This study shows that GPU general computing technology has broad application prospects in the field of remote sensing image fusion.

关键词： image fusion GPU model CUDA optimization acceleration

作者: Jin Zhao Hat fang Zhou

作者单位: School of Computer, National University of Defense Technology ChangSha, Hunan, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1652-1656

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）