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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 107-113     DOI: 10.6046/zrzyyg.2020253
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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
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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
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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.
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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
Name 型号 GeForce GT 640M LE
Compute Capability 计算能力/ TFlops 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
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
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