GPU-based parallel image processing algorithm for flood and drought monitoring
-
摘要: 针对面向汛旱情监测应用中遥感影像处理耗时过长的问题,包括辐射校正、几何纠正、遥感指数计算等过程,对其业务化工作流程进行了分解分析。结合统一计算架构(compute unified device architecture,CUDA)的存储结构和程序设计模型,将数据处理过程划分为数据读取、直方图统计、栅格分割、波段计算、重采样和数据输出等模块,对波段计算及重采样等模块设计了并行处理方案,并通过实验确定了栅格划分的最佳尺度,基于栅格数组图形处理器(graphics processing unit,GPU)映射方法加速了数据传输效率,最终提出了基于CUDA架构CPU-GPU协同的并行处理算法。实验结果表明,辐射校正及遥感指数计算的波段计算模块可节约58.9%的时间; 几何纠正效果最为显著,最邻近像元重采样和双线性内插重采样模块的最终加速比分别能够达到9倍和7倍以上。Abstract: Aiming at the time-consuming problems in the remote sensing image processing for flood and drought monitoring, the authors analyzed related workflows and algorithms including radiometric correction, geometric correction, and the calculation of remote sensing indices. Based on the storage structure and program design model of compute unified device architecture (CUDA), the remote sensing image processing was divided into several modules, including data reading, histogram statistics, grid partition, band calculation, resampling, and data output. Among them, parallel processing schemes were designed for the modules of band calculation and resampling, and the optimal cell sizes were determined for the module of grid partition. Meanwhile, the data transfer efficiency was increased through the grid data mapping based on a graphics processing unit (GPU). Finally, a parallel processing algorithm based on CPU-GPU collaboration in CUDA was proposed. The experiment results are as follows. The modules of radiometric correction and band calculation of remote sensing indices showed a 58.9% saving in time. Meanwhile, the geometric correction module enjoyed the most significant time-saving effects, and the final speedup ratios of the resampling methods of nearest neighbor and bilinear interpolation reached up to nine and seven times, respectively.
-
Key words:
- flood and drought monitoring /
- geometric correction /
- calculation of remote sensing ndex /
- GPU /
- CUDA /
-
-
[1] 肖汉, 张祖勋. 基于GPGPU的并行影像匹配算法[J]. 测绘学报, 2010, 39(1):46-51.
[2] Xiao H, Zhang Z X. Parallel image matching algorithm based on GPGPU[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(1):46-51.
[3] 方留杨, 王密, 李德仁. CPU和GPU协同处理的光学卫星遥感影像正射校正方法[J]. 测绘学报, 2013, 42(5):668-675.
[4] Fang L Y, Wang M, Li D R. A CPU-GPU co-processing orthographic rectification approach for optical satellite imagery[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(5):668-675.
[5] 张过, 潘红播, 江万寿, 等. 基于RPC模型的线阵卫星影像核线排列及其几何关系重建[J]. 国土资源遥感, 2010, 22(4):1-5.doi: 10.6046/gtzyyg.2010.04.01.
[6] Zhang G, Pan H B, Jiang W S. Epipolar resampling and epipolar geometry reconstruction of linear array scanner scenes based on RPC model[J]. Remote Sensing for Land and Resources, 2010, 22(4):1-5.doi: 10.6046/gtzyyg.2010.04.01.
[7] McFeeters S K. The use of the normalized difference water index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432.
[8] 徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 1(5):589-595.
[9] Xu H Q. A study on information extraction of water body with the modified normalized difference water index(MNDWI)[J]. Journal of Remote Sensing, 2005, 1(5):589-595.
[10] 马秀丹, 崔宾阁, 钟勇, 等. 一种基于GPU和内存映射文件的高分辨率遥感图像快速处理方法[J]. 海洋科学, 2018, 42(1):139-146.
[11] Ma X D, Cui B G, Zhong Y, et al. A fast processing method for high-resolution remote sensing image based on GPU and memory mapping file[J]. Marine Sciences, 2018, 42(1):139-146.
[12] 沈夏炯, 侯柏成, 韩道军, 等. 基于GPU和矩阵分块的增强植被指数计算[J]. 遥感信息, 2018, 33(3):63-69.
[13] Shen X J, Hou B C, Han D J, et al. Calculation of enhanced vegetation index based on GPU and matrix partition[J]. Remote Sensing Information, 2018, 33(3):63-69.
[14] NVIDIA.CUDA 2.0 for windows CUDA 2.0 programming guide[EB/OL]. [2008-06-07]. http://developer.download.nvidia.com/compute/cuda/20/docs/NVIDIA CUDA Programming Guide 2.0.pdf.20.
[15] Rajpoot P S, Kumar A. Impact assessment of meteorological drought on rainfed agriculture using drought index and NDVI modeling:A case study of Tikamgarh district[J]. Applied Geomatics, 2019, 11(1):15-23.
[16] Huang F, Chen Y, Li L, et al. Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster[J]. International Journal of Digital Earth, 2019, 12(3):328-353.
[17] Heo J M, Jo G, Han H J, et al. Accelerated code generator for processing ocean color remote sensing data on GPU[C]// IEEE.IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018:9218-9221.
[18] Nakamura T, Murakami S, Sambo L, et al. Efficacy of a GPGPU-acceleration to inundation flow simulation in tonle sap lake in cambodia[J]. Engineering Journal, 2019, 23(1):151-169.
[19] Zhou H F, Zhao J. Parallel programming design and storage optimization of remote sensing image registration based on GPU[J]. Journal of Computer Research and Development, 2012, 49(1):281-286.
[20] Guo X, Wu J, Wu Z, et al. Parallel computation of aerial target reflection of background infrared radiation:Performance comparison of OpenMP,OpenACC,and CUDA implementations[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(4):1653-1662.
[21] Gegout P, Oberlé P, Desjardins C, et al. Ray-tracing of GNSS signal through the atmosphere powered by CUDA,HMPP and GPUs technologies[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(5):1592-1602.
[22] Norman M, Larkin J, Vose A, et al. A case study of CUDA FORTRAN and OpenACC for an atmospheric climate kernel[J]. Journal of Computational Science, 2015, 9(1):1-6.
[23] Peña A J, Reaño C, Silla F, et al. A complete and efficient CUDA-sharing solution for HPC clusters[J]. Parallel Computing, 2014, 40(10):574-588.
-
计量
- 文章访问数: 465
- PDF下载数: 142
- 施引文献: 0
下载: