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An improved multispectral image segmentation method based on super-pixels |
Yongmei ZHANG1, Haiyan SUN1, Yulong XU2 |
1.Computer College, North China University of Technology, Beijing 100144,China 2.Section Steel Mill of Tisco, Taiyuan, 030003, China |
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Abstract In the object-oriented multispectral image segmentation, the initial object feature may not reflect the global feature of the whole region and can lead to an incorrect merge. To solve the problem, this paper proposes a method that combines the result of the simple linear iterative clustering(SLIC) super pixel and the rough segmentation result of structure tensor. First, the SLIC process is executed to get an over-segmentation result. Then, make sure the feature of the initial object of the fractal net evolution approach can reflect the real distribution of the whole region, and do the pre-merging between the super pixels under the control of the rough segmentation result of the structure tensor in the scale space. This process can enhance the anti-noise capability of the following merging process. Finally, the final results are given; compared with the results of the traditional fractal net evolution approach(FNEA), the result shows that the method proposed in the paper has better anti-noise capability, and can get better segmentation results even in handling the complex city multispectral images.
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Keywords
super pixel
multispectral image
image segmentation
fractal net evolution approach
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Issue Date: 15 March 2019
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