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

被引:0
作者
Tianjun Wu
Liegang Xia
Jiancheng Luo
Xiaocheng Zhou
Xiaodong Hu
Jianghong Ma
Xueli Song
机构
[1] Chang’an University,Department of Mathematics and Information Science, College of Science
[2] Fuzhou University,Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education
[3] Zhejiang University of Technology,College of Computer Science and Technology
[4] Chinese Academy of Sciences,State Key Laboratory of Remote Sensing Sciences, Institute of Remote Sensing and Digital Earth
[5] State Key Laboratory of Geo-Information Engineering,undefined
来源
Journal of the Indian Society of Remote Sensing | 2018年 / 46卷
关键词
High-resolution remote sensing images; Image segmentation; Mean-shift; Parallel computation; Data-partitioning;
D O I
暂无
中图分类号
学科分类号
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.
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页码:1805 / 1814
页数:9
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