Paper Infomation
Parallel Computing Reasearch of Normalized Difference Vegetation Index Based on OpenMP and OpenCV
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Author: Xianyu Zuo, Dongdong Shang, Beibei Li, Minghao Xiong, Xiangzhi Huang
Abstract: The study of normalized difference vegetation index (NDVI) is the main field of remote sensing application, and also an important issue in the field of remote sensing. With the progress of remote sensing in China, the high resolution satellite developed by ourselves has been launched in succession, which has accelerated and expanded the application range of high resolution satellite images produces a large number of remote sensing images. High resolution remote sensing images produced by satellites every day also show exponential growth, reaching the order for Terabyte (TB) even Petabyte (PB) level. However, the traditional serial vegetation index extraction algorithm can’t extract the vegetation index from these massive remote sensing data in time and effectively. Based on this, this paper will use the normalized vegetation index (NDVI) algorithm as an example, Optimization of the NDVI algorithm, which based on the support of the high performance research platform and parallel computing technology, achieved the parallel computation method of NDVI fast extraction through C++ programming language library function calling OpenMP and OpenCV and verified the validity of NDVI parallel extraction algorithm by GF-1 data.
Keywords: Normalized Difference Vegetation Index, NDVI Algorithm, Parallel Computing, OpenMP, OpenCV
References:
[1] SiTao FU,Yun Zhou,Research on normalized difference vegetation index algorithm based on Remote Sensing Images[J].Surveying and Mapping in Jiangxi,2010,84(03):31-33.
[2] LongFei Wang,Application of homemade satellite data in remote sensing investigation of geological hazards[D].BeiJing:China University of Geosciences,2014.
[3] Yi Xie,Research on organization structure of massive remote sensing image data storage[D].HeNan:Henan University,2011.
[4] YingHui Zhao,CongFeng Jiang.Research on high performance parallel processing technology of remote sensing images[J].Computer technology and development,2014,24(7):202-203.
[5] YaoHua Luo.Application of high performance computing in hyperspectral remote sensing data processing[D].ChengDu:Chengdu University of Technology,2013.
[6] FangZheng Chang,YinTi Zhao,ShanLei Liu.Design of CUDA parallel algorithm for CVA change detection in remote sensing images[J].Journal of remote sensing.2016,20(1):114-128.
[7] Tang M,Zhao J Y,Tong R F,et al.GPU accelerated convex hull computation[J]. Computers & Graphics.2012, 36(5):498–506.
[8] XianYu Zuo,He Meng,Ning Li,He Zhang.Research on remote sensing image NDVI algorithm based on CPU-GPU heterogeneous platform[C].JiangSu WuXi:Proceedings of the Annual Academic Conference on high performance computing in China,2015.
[9] Feng Zhang,BingFang Wu,ChengLin Liu.Remote sensing extraction method for regional crop growth process[J].Journal of remote sensing.2004,8(6):515-516.
[10] ChunCheng Han,HePing Lin,Tao Zhou.Research on remote sensing digital image enhancement method[J].Computer science research.2012,31(12):29-31.
[11] ZengShou Dong,FengChun Zhang,MingJun Liu.Study on enhancement processing method of satellite remote sensing images[J].Computer Simulation.2009,4(26):249-252.
[12] Ying Xiong,Qiong Luo.Parallel implementation of NDVI algorithm based on OpenCL[J].Software and application of computer CD.2013,2013(18):98-99.
[13] Xi Peng,Bing Gen Gu,ZhanTao Li.Design of OpenMP parallel program based on multi-core[J].silicon valley.2010,2010(16):97-98.
[14] Jin Zhu.Research on Optimization Algorithm of wheelset image online processing algorithm based on parallel programming[D].ZheJiang:Hangzhou Dianzi University,2015.
[15] ShouMei Sun,JingHui Wang.Brief introduction of ant colony algorithm principle and pseudo code realization[J].Computer knowledge and technology.2014,10(10):2253-2255.