Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm
Oil pipeline is a kind of high-risk continuous transportation system. High consequence area refers to the area where public life as well as property are endangered and even the environment is polluted after pipeline leakage. Through the analysis of remote sensing images, the position of oil pipeline...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-09-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2021.1959306 |
| _version_ | 1797661127802355712 |
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| author | Fengcai Huo Shuai Dong Weijian Ren Xueting Sun |
| author_facet | Fengcai Huo Shuai Dong Weijian Ren Xueting Sun |
| author_sort | Fengcai Huo |
| collection | DOAJ |
| description | Oil pipeline is a kind of high-risk continuous transportation system. High consequence area refers to the area where public life as well as property are endangered and even the environment is polluted after pipeline leakage. Through the analysis of remote sensing images, the position of oil pipeline and the change of its surrounding environment can be determined, and the monitoring and protection of oil pipeline in high consequence area can be realized. Aiming at the problems of low segmentation accuracy, difficulty in obtaining global optimal solution and low efficiency caused by prior knowledge of classical Markov image segmentation. A fuzzy Markov random field algorithm based on artificial bee colony strategy is proposed. Firstly, according to the initial image segmentation results, pixels are divided into definite points and fuzzy points, and only fuzzy points are calculated. Secondly, a Markov algorithm based on artificial bee colony strategy is designed, which can adaptively select potential function parameters for different images. Finally, the improved algorithm is applied to remote sensing image segmentation in high consequence area of oil pipeline. By comparing multiple images, performance parameters and algorithms, it is proved that the improved algorithm has better optimization ability and convergence performance. |
| first_indexed | 2024-03-11T18:39:59Z |
| format | Article |
| id | doaj.art-98c68eb28f4747e697fc38c0eac1f299 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:39:59Z |
| publishDate | 2021-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-98c68eb28f4747e697fc38c0eac1f2992023-10-12T13:36:24ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712021-09-0147574977210.1080/07038992.2021.19593061959306Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF AlgorithmFengcai Huo0Shuai Dong1Weijian Ren2Xueting Sun3School of Electrical Information Engineering, Northeast Petroleum UniversitySchool of Electrical Information Engineering, Northeast Petroleum UniversitySchool of Electrical Information Engineering, Northeast Petroleum UniversitySchool of Electrical Information Engineering, Northeast Petroleum UniversityOil pipeline is a kind of high-risk continuous transportation system. High consequence area refers to the area where public life as well as property are endangered and even the environment is polluted after pipeline leakage. Through the analysis of remote sensing images, the position of oil pipeline and the change of its surrounding environment can be determined, and the monitoring and protection of oil pipeline in high consequence area can be realized. Aiming at the problems of low segmentation accuracy, difficulty in obtaining global optimal solution and low efficiency caused by prior knowledge of classical Markov image segmentation. A fuzzy Markov random field algorithm based on artificial bee colony strategy is proposed. Firstly, according to the initial image segmentation results, pixels are divided into definite points and fuzzy points, and only fuzzy points are calculated. Secondly, a Markov algorithm based on artificial bee colony strategy is designed, which can adaptively select potential function parameters for different images. Finally, the improved algorithm is applied to remote sensing image segmentation in high consequence area of oil pipeline. By comparing multiple images, performance parameters and algorithms, it is proved that the improved algorithm has better optimization ability and convergence performance.http://dx.doi.org/10.1080/07038992.2021.1959306 |
| spellingShingle | Fengcai Huo Shuai Dong Weijian Ren Xueting Sun Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm Canadian Journal of Remote Sensing |
| title | Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm |
| title_full | Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm |
| title_fullStr | Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm |
| title_full_unstemmed | Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm |
| title_short | Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm |
| title_sort | remote sensing image segmentation of pipeline high consequence area based on bee colony strategy fuzzy mrf algorithm |
| url | http://dx.doi.org/10.1080/07038992.2021.1959306 |
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