Please wait a minute...
 
Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 107-113     DOI: 10.6046/zrzyyg.2020253
|
GPU-based parallel image processing algorithm for flood and drought monitoring
ZHAO Xiaochen1(), WU Haonan2, LI Linyi1, MENG Lingkui1()
1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2. Pearl River Comprehensive Technology and Network Information Center, Guangzhou 510611, China
Download: PDF(3919 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
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.
Keywords flood and drought monitoring      geometric correction      calculation of remote sensing ndex      GPU      CUDA     
ZTFLH:  TP751P237  
Corresponding Authors: MENG Lingkui     E-mail: zhaoxiaochen@whu.edu.cn;Lkmeng@whu.edu.cn
Issue Date: 24 September 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Xiaochen ZHAO
Haonan WU
Linyi LI
Lingkui MENG
Cite this article:   
Xiaochen ZHAO,Haonan WU,Linyi LI, et al. GPU-based parallel image processing algorithm for flood and drought monitoring[J]. Remote Sensing for Natural Resources, 2021, 33(3): 107-113.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020253     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/107
Fig.1  Geometric correction algorithm of RPC model
Fig.2  Parallel mode of remote sensing image processing algorithm
Fig.3  Parallel RPC algorithm
字段 含义 值
Name 型号 GeForce GT 640M LE
Compute Capability 计算能力/ TFlops① 2.1
Warp Size 线程束内线程数量/个 32
Threads Per Block 线程块最大线程数/个 1 024
Threads Per Multiprocessor 流处理器簇最大线程数/个 1 536
Tab.1  GPU parameters
项目 值
型号 GeForce GT 640M LE
计算能力/ TFlops 2.1
时钟频率/ MHz 1 505
总线带宽/ bits 128
流处理器簇 2
流处理器(CUDA核心) 96
流处理器簇最大线程数/个 1 536
线程束尺寸 32
线程块尺寸 1 024
线程块寄存器数量/个 3 268
驱动版本 376.51
CUDA版本 8.0
CUDA运行库版本 6.5
Tab.2  GPU and CUDA parameters
Fig.4  Different thread block sizes
Fig.5  Grid scale of performance
Fig.6  Processing time contrast of CPU and GPU of index
Fig.7  Remote sensing index in Poyang Lake
波段号 影像读取 直方图统计 波段运算 产品输出 合计
传统串行 CUDA并行 传统串行 CUDA并行 传统串行 CUDA并行 传统串行 CUDA并行 传统串行 CUDA并行
B1 2.76 3.57 32.71 31.7 16.34 7.54 2.90 3.70 54.71 46.51
B2 3.42 3.29 28.76 40.29 22.90 8.30 3.16 2.21 58.24 54.09
B3 8.35 4.57 40.23 37.75 17.95 10.48 3.21 2.39 69.74 55.19
B4 4.57 7.63 22.95 39.32 14.21 6.54 1.98 3.25 43.71 56.74
B5 6.54 9.47 34.69 30.07 26.30 9.36 4.32 5.73 71.85 54.63
B6 2.32 3.12 32.81 31.60 18.68 10.23 6.31 4.16 60.12 49.11
B7 3.21 2.67 39.65 28.40 19.73 6.98 3.29 5.27 65.88 43.32
B8 7.48 4.32 27.03 38.27 20.10 4.38 2.70 4.90 57.31 51.87
B9 3.24 6.32 26.54 33.91 19.43 7.49 2.40 3.57 51.61 51.29
B10 5.31 2.13 35.63 29.50 27.31 7.90 2.98 4.78 71.23 44.31
B11 3.59 4.36 31.26 35.10 15.49 10.61 2.67 2.90 53.01 52.97
均值 4.62 4.68 32.02 34.17 19.86 8.16 3.27 3.90 59.76 50.91
Tab.3  Processing time of CPU and CUDA for radiometric correction(s)
Fig.8  Processing time contrast of CPU and GPU for RPC
重采样方式 步骤 串行/s 并行/s 加速比
最邻近像元 重采样 174.47 18.79 9.29
总计 195.88 61.96 3.16
双线性内插 重采样 506.32 71.36 7.10
总计 529.60 118.75 4.46
Tab.4  Speedup radio of parallel RPC algorithm
[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.
[1] Xiao H, Zhang Z X. Parallel image matching algorithm based on GPGPU[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(1):46-51.
[2] 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.
[2] 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.
[3] 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.
doi: 10.6046/gtzyyg.2010.04.01
[3] 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.
doi: 10.6046/gtzyyg.2010.04.01
[4] 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.
doi: 10.1080/01431169608948714 url: https://www.tandfonline.com/doi/full/10.1080/01431169608948714
[5] 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.
[5] 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.
[6] 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.
[6] 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.
[7] 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.
[7] 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.
[8] 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.
url: http://developer.download.nvidia.com/compute/cuda/20/docs/NVIDIA CUDA Programming Guide 2.0.pdf.20
[9] 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.
doi: 10.1007/s12518-018-0230-6 url: https://doi.org/10.1007/s12518-018-0230-6
[10] 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.
doi: 10.1080/17538947.2018.1432709 url: https://www.tandfonline.com/doi/full/10.1080/17538947.2018.1432709
[11] 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.
[12] 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.
doi: 10.4186/ej url: https://www.engj.org/
[13] 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.
[14] 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.
doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[15] 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.
doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[16] 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.
doi: 10.1016/j.jocs.2015.04.022 url: https://linkinghub.elsevier.com/retrieve/pii/S1877750315000605
[17] 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.
doi: 10.1016/j.parco.2014.09.011 url: https://linkinghub.elsevier.com/retrieve/pii/S0167819114001227
[1] MA Yutang, PAN Hao, ZHOU Fangrong, HUANG Ran, ZHAO Jianeng, LUO Jiqiang, LIU Jing, SUN Haoxuan, JIA Weijie, ZHANG Tao. Method to calibrate the coordinates of transmission towers based on satellite images[J]. Remote Sensing for Natural Resources, 2022, 34(2): 63-71.
[2] LI Penglong, DING Yi, HU Yan, LUO Ding, DUAN Songjiang, SHU Wenqiang. A method for rapid UAV images mosaicking based on GPU parallel computing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 57-63.
[3] JIA Yi, WANG Sheng, JIANG Wanshou. Algorithm of Bowtie effect rapid removing for low and medium resolution satellite images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 83-87.
[4] MA Lichun, YANG Renzhong, SHI Lu. Improvement and implementation of Forman phase correction algorithm[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 97-101.
[5] LIANG Zhi-hua. A Study of Algorithm of Geometric Processing for MODIS Image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(1): 8-12.
[6] FENG Yong-Jiu, HAN Zhen. Remote Sensing Based Spatio-temporal Evolution of Land Use Pattern in Huangpu River Coast[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 91-96.
[7] WAN Zi, XU Mao-Song, XIA Zhong-Sheng, ZHANG Feng-Li, GONG Hua-Ze. The Dual-aspect Geometric Correction Method Based on DEM for High-resolution SAR Images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 12-16.
[8] TANG Yan-Hong, HAO Yan-Ling, LU Zhi-Zhong. THE DATA CORRECTION OF SEA CLUTTER IN THE
WAVE MONITORING SYSTEM OF NAUTICAL RADAR[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(3): 7-11.
[9] SONG Wei. AN EXPERIMENTAL STUDY OF THE GEOMETRIC CORRECTION METHOD FOR CBERS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(1): 51-54.
[10] CHEN Hua, AN Na, YANG Qing-Hua. GEOMETRIC CORRECTION METHODS OF SPOT5 1A DATA BASED
ON GPS-MEASURED GROUND CONTROL POINTS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2007, 19(4): 47-50.
[11] Guo Xiaofang, Wang Tianxing, Zhang Youying, Sun Bingzhao, Yang Suming. DEVELOPING OF PRACTICAL AMSS IMAGES PRODUCTION SYSTEM[J]. REMOTE SENSING FOR LAND & RESOURCES, 1997, 9(3): 1-7，14.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech