Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

作     者：Wu, Tianjun Xia, Liegang Luo, Jiancheng Zhou, Xiaocheng Hu, Xiaodong Ma, Jianghong Song, Xueli 

作者机构：Changan Univ Dept Math & Informat Sci Coll Sci Xian 710064 Shaanxi Peoples R China Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350002 Fujian Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China State Key Lab Geoinformat Engn Xian 710054 Shaanxi Peoples R China 

出 版 物：《JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING》 (印度遥感学会杂志)

年 卷 期：2018年第46卷第11期

页      面：1805-1814页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Natural Science Foundation of China [41631179, 41601437] National Key Research and Development Program [2017YFB0503600] Natural Science Basic Research Plan in Shaanxi Province of China [2017JQ4002] Open Projects of Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University [2018LSDMIS03] State Key Laboratory of Geo-information Engineering [SKLGIE2017-Z-4-3] Special Fund for Basic Scientific Research of Central Colleges in Chang'an University 

主　　题：High-resolution remote sensing images Image segmentation Mean-shift Parallel computation Data-partitioning 

摘      要：In high-resolution remote sensing image processing, segmentation is a crucial step that extracts information within the object-based image analysis framework. Because of its robustness, mean-shift segmentation algorithms are widely used in the field of image segmentation. However, the traditional implementation of these methods cannot process large volumes of images rapidly under limited computing resources. Currently, parallel computing models are generally employed for segmentation tasks with massive remote sensing images. This paper presents a parallel implementation of the mean-shift segmentation algorithm based on an analysis of the principle and characteristics of this technique. To avoid the inconsistency on the boundaries of adjacent data chunks, we propose a novel buffer-zone-based data-partitioning strategy. Employing the proposed data-partitioning strategy, two intensively computation steps are performed in parallel on different data chunks. The experimental results show that the proposed algorithm effectively improves the computing efficiency of image segmentation in a parallel computing environment. Furthermore, they demonstrate the practicality of massive image segmentation when computer resources are limited.