利用区域信息的航拍图像分割

杨名宇; 李刚

doi:10.3788/CO.20140705.0779

Volume 7 Issue 5

Sep. 2014

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Article Contents

Chinese Optics > 2014 > 7(5): 779-785.

YANG Ming-yu, LI Gang. Aerial image segmentation with region information[J]. Chinese Optics, 2014, 7(5): 779-785. doi: 10.3788/CO.20140705.0779

Citation:

YANG Ming-yu, LI Gang. Aerial image segmentation with region information[J]. Chinese Optics, 2014, 7(5): 779-785. doi: 10.3788/CO.20140705.0779

YANG Ming-yu, LI Gang. Aerial image segmentation with region information[J]. Chinese Optics, 2014, 7(5): 779-785. doi: 10.3788/CO.20140705.0779

Citation:

YANG Ming-yu, LI Gang. Aerial image segmentation with region information[J]. Chinese Optics, 2014, 7(5): 779-785. doi: 10.3788/CO.20140705.0779

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Aerial image segmentation with region information

doi: 10.3788/CO.20140705.0779

YANG Ming-yu^{1,2
,
,},
LI Gang^1,2

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
2.
Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Sciences, Changchun 130033, China

Received Date: 14 Apr 2014
Rev Recd Date: 16 Jun 2014
Publish Date: 25 Sep 2014

Abstract

Abstract

Image segmentation based on level set method is one of the most widely used methods in segmentation domain. Due to a large field of view for aerial image, traditional methods usually can not obtain a global segmentation, and segmentation result is often very poor. In this paper, a new segmentation method with region information for aerial image is proposed. First, a new signed press function is proposed to enhance the capture range, which also can obtain global segmentation result. Second, compared with Chan-Vese model, proposed model is not limited by initial condition, and can enhance the capture range further. Third, proposed model utilizes the region information of an image, which can automatically segment inside and outside of an object simultaneously. In the proposed model, level set function doesn't need to be initialized to signed distance function, which can reduce a lot of computation cost. Moreover, the proposed model has no curvature item compared with traditional level set method, and is easy for numerical implementation. Experiment results demonstrate that proposed model has a higher accuracy of 3%, and 6 times faster than GAC model and Chan-Vese model.
- image segmentation,
- aerial image,
- region information

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References(1)

References

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