A GPU-Based Parallel Algorithm for Landscape Metrics

被引：0
|
作者：
Zhong A. [1 ,2 ]
Chang L. [1 ]
Ma Y. [1 ,2 ]
Kang M. [1 ,2 ]
Mao Z. [1 ]
机构：
[1] School of Resource and Environmental Sciences, Wuhan University, Wuhan
[2] Institute of Smart Perception and Intelligent Computing, Wuhan University, Wuhan
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2020年 / 45卷 / 06期
关键词：
Connected component labeling algorithm; Graphics processing unit(GPU); Landscape metrics; Parallel computing;
D O I：
10.13203/j.whugis20190095
中图分类号：
学科分类号：
摘要：
Massive spatial data poses increasing challenges to traditional analysis software. For example, landscape pattern analysis software FRAGSTATS has been unable to process provincial-level high-resolution land cover data. Based on Two-Pass connected component labeling algorithm, this paper provides an improved parallel algorithm with GPU programming to solve the landscape metrics computation problem about massive land use data. This parallel algorithm for massive landscape metrics calculation takes full advantage of a general computer, and focuses on patch perimeter and area calculation. It can also accelerate computation speed by multithreading and iteration times reduction to decrease computation time than traditional serial algorithms. We apply the proposed algorithm and serial algorithm to calculate landscape metrics of the land use classification raster images at different resolutions under patch scale.The experiment result shows great improvement of calculation performance of landscape metrics, and the efficiency has been improved by 5 times comparing with the serial algorithm, which proves that our proposed algorithm is a better choice for landscape analysis of massive data. © 2020, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.
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收藏
页码：941 / 948
页数：7
相关论文
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