Object-oriented remote sensing image segmentation based on automatic multiseed region growing algorithm
For the segmentation of a remote sensing image, the seeded region growing algorithm is a common method. The traditional single-seed region growing algorithm can only segment a remote sensing image in a single, continuous object with simple texture. However, in the case of a high-resolution remote se...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Science Press
2017-11-01
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| Series: | 工程科学学报 |
| Subjects: | |
| Online Access: | http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2017.11.017 |
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| Summary: | For the segmentation of a remote sensing image, the seeded region growing algorithm is a common method. The traditional single-seed region growing algorithm can only segment a remote sensing image in a single, continuous object with simple texture. However, in the case of a high-resolution remote sensing image with complex texture and multispectral features, the segmentation result of this algorithm is unsatisfactory, as it cannot segment multiple objects simultaneously and effectively. To solve this problem, this paper proposes an improved object-oriented automatic multiseed region growing algorithm, which is suitable for simultaneously extracting multiple target objects and its segmentation result is also good. The method first uses an improved median filter to smooth the image, making the interior of the multiple target objects homogeneous, while preserving their texture. Then, it automatically selects the multiple seed regions through a certain criterion and finally, processes the grown regions and combines them. Thus, this paper obtains the segmentation results of various objects. The paper uses three sets of aerial images with different spatial resolutions to carry out experiments. Compared with watershed algorithm and traditional single-seed region growing algorithm, this method can be used for global objects. It can automatically select different types of seeds with multiple features and can simultaneously segment multiple target objects, thus providing a reliable data for the object-oriented image analysis and application. |
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| ISSN: | 2095-9389 |