Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images |
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| Authors: | Tianjun Wu Liegang Xia Jiancheng Luo Xiaocheng Zhou Xiaodong Hu Jianghong Ma Xueli Song |
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| Institution: | 1.Department of Mathematics and Information Science, College of Science,Chang’an University,Xi’an,People’s Republic of China;2.Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education,Fuzhou University,Fuzhou,People’s Republic of China;3.College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou,People’s Republic of China;4.State Key Laboratory of Remote Sensing Sciences, Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,People’s Republic of China;5.State Key Laboratory of Geo-Information Engineering,Xi’an,People’s Republic of China |
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| Abstract: | 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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