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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 13-18     DOI: 10.6046/gtzyyg.2020.01.03
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Segmentation of large scale remote sensing image based on Mean Shift
Shicai ZHU1, Xiaotong ZHAI2, Zongwei WANG1()
1. Jiangsu Province Surveying and Mapping Engineering Institute, Nanjing 210013, China
2. Jiangsu Province Archives of Surveying and Mapping Production, Nanjing 210013, China
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Abstract  

Mean Shift algorithm has been widely used in image segmentation because of its fast convergence speed and good segmentation accuracy. However, when large scale remote sensing images are processed, Mean Shift algorithm has some problems, such as slow speed and low efficiency. In this paper, a parallel seamless segmentation algorithm based on Mean Shift is proposed. The algorithm is based on block parallel Mean Shift segmentation. The elimination criterion of block lines is determined by uniform coding of label images and establishing corresponding relations between label values of overlapping regions. Then, the row and column directions of the label image are stitched together. Finally, the segmented label image is vectorized to generate the final segmentation result. Compared with the original Mean Shift algorithm, the algorithm put forward in this paper can not only ensure the reliability of segmentation results but also greatly improve the efficiency of image segmentation, and can also solve the problem of large scale remote sensing image segmentation.
Keywords Mean Shift algorithm      image segmentation      parallel computing      overlapping area      seamless stitch     
:  TP79  
Corresponding Authors: Zongwei WANG     E-mail: wangzongwei328@163.com
Issue Date: 14 March 2020
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Shicai ZHU
Xiaotong ZHAI
Zongwei WANG
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Shicai ZHU,Xiaotong ZHAI,Zongwei WANG. Segmentation of large scale remote sensing image based on Mean Shift[J]. Remote Sensing for Land & Resources, 2020, 32(1): 13-18.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.01.03     OR     https://www.gtzyyg.com/EN/Y2020/V32/I1/13
Fig.1  Flow chart of the algorithm
Fig.2  Data blocking and overlapping areas
Fig.3  Flow chart for establishing label value correspondence in overlapping areas
Fig.4  Comparison of experimental results of ZY-3
影像类型 影像大
小/像素		 空间分
辨率/m		 原始算法
耗时/s		 分块
数量		 本文算法
耗时/s
资源三号 840×840 2 2.6 4 3.1
Pleiades 1 500×1 500 0.5 6.1 9 5.7
北京二号 4 000×4 000 1 44.3 16 26.7
高景一号 8 000×8 000 0.5 157.9 64 91.4
高分二号 15 000×15 000 1 — 225 309.5
Tab.1  Time-consuming comparison of segmentation experiments
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