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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 86-95     DOI: 10.6046/gtzyyg.2020124
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High-resolution remote sensing image segmentation based on improved superpixel and marker watershed
ZHANG Rui1(), YOU Shucheng1(), DU Lei1, LU Jing1, HE Yun1, HU Yong2
1. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
2. Chongqing Institute of Surveying and Monitoring for Planning and Natural Resources, Chongqing 400120, China
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Abstract  

Image segmentation is a key step in object-oriented analysis of high resolution images and plays an important role in information extraction accuracy. In order to improve the segmentation performance of object-oriented algorithms for high-resolution remote sensing images, this paper proposes a segmentation method (PCSLIC-MW) to improve the superpixel and marker watershed, including feature fusion, superpixel initial segmentation, and control marker watershed segmentation. In the phase of superpixel segmentation, a new distance measure calculation rule is proposed, which combines color space, spatial position information and phase consistency texture feature. And then the gray value of each patch is calculated after superpixel segmentation, image reconstruction after segmentation, and morphological extension technology is used to extract local minimum (H-minima) so as to control the number of segmentation regions. The over-cutting produced by the traditional mathematical morphologic watershed segmentation algorithm is optimized and improved. The reconstructed image is conducted by Gaussian filter, and then the control marker watershed algorithm is used to re-segment the reconstructed image. For experiment, ZY3-02 satellite image and airborne aerial image are adopted to verify the proposed method, the precision and recall rate are used to evaluate the segmentation accuracy, and the results are compared with those of other segmentation methods to prove the segmentation effectiveness of the proposed method.
Keywords high-resolution image      watershed      superpixel      ZY3-02     
ZTFLH:  TP79  
Corresponding Authors: YOU Shucheng     E-mail: ruizh581@163.com;youusc@126.com
Issue Date: 18 March 2021
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Rui ZHANG
Shucheng YOU
Lei DU
Jing LU
Yun HE
Yong HU
Cite this article:   
Rui ZHANG,Shucheng YOU,Lei DU, et al. High-resolution remote sensing image segmentation based on improved superpixel and marker watershed[J]. Remote Sensing for Land & Resources, 2021, 33(1): 86-95.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020124     OR     https://www.gtzyyg.com/EN/Y2021/V33/I1/86
Fig.1  Search area of super pixel
Fig.2  Procedure of PCSLIC-MW
Fig.3  Worldview 2 multispectral image of 0.5 m resolution
Fig.4-1  Segmentation results of PCSLIC-MW method
Fig.4-2  Segmentation results of PCSLIC-MW method
Fig.5  Local results of segmentation experiments based on Worldview 2
方法 精确度 召回率
PCSLIC-MW 0.83 0.85
FNEA(100) 0.72 0.79
FNEA(80) 0.68 0.75
FNEA(60) 0.75 0.80
FNEA(40) 0.65 0.81
FNEA(25) 0.64 0.76
Tab.1  Accuracy evaluation of segmentation experiment based on Worldview 2 image
平台 相机模式 波段 光谱范围/nm 空间分辨率/m
全色正视 — 450~800 2.1
蓝光 450~520
ZY3-02
多光谱正视		 绿光 520~590
5.8
红光 630~690
近红外 770~890
Tab.2  Parameters of ZY3-02 multispectral image
Fig.6  ZY3-02 multispectral image of 2.1 m resolution
Fig.7-1  Segmentation results of PCSLIC-MW method
Fig.7-2  Segmentation results of PCSLIC-MW method
Fig.8  Local results of segmentation experiments based on ZY3-02
方法 精确度 召回率
PCSLIC-MW 0.86 0.90
FNEA(100) 0.69 0.73
FNEA(80) 0.70 0.74
FNEA(60) 0.73 0.83
FNEA(40) 0.68 0.76
FNEA(25) 0.68 0.75
Tab.3  Accuracy evaluation of segmentation experiment based on ZY3-02 image
Fig.9  Comparison of segmentation methods
方法 精确度 召回率
PCSLIC-MW 0.90 0.93
MS分割 0.72 0.79
FNEA(100) 0.65 0.80
FNEA(80) 0.68 0.76
FNEA(50) 0.63 0.79
Tab.4  Accuracy evaluation of different segmentation methods
影像 空间分辨率/m 精确度 召回率
Worldview 2融合影像 0.5 0.91 0.93
ZY3-02融合影像 2.1 0.90 0.93
ZY3-02多光谱影像 5.8 0.68 0.65
Tab.5  Effect of image resolution difference on segmentation accuracy
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