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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 72-79     DOI: 10.6046/gtzyyg.2019.03.10
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A high resolution remote sensing image segmentation method based on superpixel and graph theory
Bingxiu YAO1, Liang HUANG1,2(), Yansong XU1
1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093,China
2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China
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Abstract  

Superpixel segmentation has become a new hotspot in remote sensing image preprocessing, but it has the problem of over segmentation. To solve this problem, the authors propose a high resolution remote sensing image segmentation method combining superpixel and graph theory. First, the simple linear iterative clustering (SLIC) algorithm is used to divide the image into superpixels, then the superpixels are merged by the graph theory algorithm, the local variance corresponding to the combined number of the merged numbers are calculated, and the appropriate image segmentation number is determined. Finally, according to the appropriate image segmentation number, the graph theory algorithm is used to recluster and merge the superpixels. Four groups of remote sensing images of different scenes and different spatial resolutions were selected as experimental data. The qualitative and quantitative analysis of experimental results was evaluated. Experimental results show that the proposed method can effectively overcome the effect of over segmentation results and achieve good segmentation results.
Keywords high spatial resolution remote sensing image      superpixel      image segmentation      simple linear iterative clustering      graph theory     
:  TP79  
Corresponding Authors: Liang HUANG     E-mail: kmhuangliang@163.com
Issue Date: 30 August 2019
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Bingxiu YAO
Liang HUANG
Yansong XU
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Bingxiu YAO,Liang HUANG,Yansong XU. A high resolution remote sensing image segmentation method based on superpixel and graph theory[J]. Remote Sensing for Land & Resources, 2019, 31(3): 72-79.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.10     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/72
Fig.1  Pixel search scope
Fig.2  Image data and experimental results of test1
方法 Precision Recall
FNEA(50) 0.714 9 0.802 6
FNEA(100) 0.723 4 0.824 8
本文方法 0.805 6 0.919 6
Tab.1  Segmentation results evaluation of test1
Fig.3  Image data and experimental results of test2
方法 Precision Recall
FNEA(50) 0.767 9 0.823 6
FNEA(100) 0.745 6 0.896 3
本文方法 0.856 3 0.956 8
Tab.2  Segmentation results evaluation of test2
Fig.4  Image data and experimental results of test3
方法 Precision Recall
FNEA(50) 0.756 9 0.819 6
FNEA(100) 0.768 9 0.879 2
本文方法 0.841 2 0.940 5
Tab.3  Segmentation results evaluation of test3
Fig.5  Image data and experimental results of test4
方法 Precision Recall
FNEA(50) 0.698 2 0.752 6
FNEA(100) 0.724 3 0.798 6
本文方法 0.801 3 0.855 6
Tab.4  Segmentation results evaluation of test4
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