Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization
Forests play a vital role in increasing carbon sequestration in the biosphere. In recent years, segmenting forest canopy images in order to obtain various plant population parameters has become an essential means to assess the ecosystem. The objective of forest canopy image segmentation is to separa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/11/1746 |
| _version_ | 1797468232922169344 |
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| author | Jingyu Wang Liangkuan Zhu Bowen Wu Arystan Ryspayev |
| author_facet | Jingyu Wang Liangkuan Zhu Bowen Wu Arystan Ryspayev |
| author_sort | Jingyu Wang |
| collection | DOAJ |
| description | Forests play a vital role in increasing carbon sequestration in the biosphere. In recent years, segmenting forest canopy images in order to obtain various plant population parameters has become an essential means to assess the ecosystem. The objective of forest canopy image segmentation is to separate and extract sky regions from the background. This study proposes a hybrid method based on improved tuna swarm optimization (ITSO) for forestry canopy image segmentation. The symmetric cross-entropy is introduced to perform forestry canopy image thresholding by modeling the classes of an image as membership functions. In order to achieve the optimal thresholds of the forest canopy image, the entropy-solving procedure is arduous and time-consuming. In order to resolve this issue, the ITSO method was adopted to search for the most significant threshold. Meanwhile, the Tent chaotic map is used to initialize the tuna population according to the chaotic factor. The experiment is carried out on four different types of forest canopy images, with four indices (MAE, RVD, IoU, and ASD) used for quantitative analysis. The experiment’s results show that the ITSO-based segmentation method outperforms others, making it a better way to segment images of forest canopies. |
| first_indexed | 2024-03-09T19:04:31Z |
| format | Article |
| id | doaj.art-69b31b158d31442295d71dea8f435213 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T19:04:31Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-69b31b158d31442295d71dea8f4352132023-11-24T04:42:31ZengMDPI AGForests1999-49072022-10-011311174610.3390/f13111746Forestry Canopy Image Segmentation Based on Improved Tuna Swarm OptimizationJingyu Wang0Liangkuan Zhu1Bowen Wu2Arystan Ryspayev3College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaForests play a vital role in increasing carbon sequestration in the biosphere. In recent years, segmenting forest canopy images in order to obtain various plant population parameters has become an essential means to assess the ecosystem. The objective of forest canopy image segmentation is to separate and extract sky regions from the background. This study proposes a hybrid method based on improved tuna swarm optimization (ITSO) for forestry canopy image segmentation. The symmetric cross-entropy is introduced to perform forestry canopy image thresholding by modeling the classes of an image as membership functions. In order to achieve the optimal thresholds of the forest canopy image, the entropy-solving procedure is arduous and time-consuming. In order to resolve this issue, the ITSO method was adopted to search for the most significant threshold. Meanwhile, the Tent chaotic map is used to initialize the tuna population according to the chaotic factor. The experiment is carried out on four different types of forest canopy images, with four indices (MAE, RVD, IoU, and ASD) used for quantitative analysis. The experiment’s results show that the ITSO-based segmentation method outperforms others, making it a better way to segment images of forest canopies.https://www.mdpi.com/1999-4907/13/11/1746forestry canopyimage segmentationsymmetric cross-entropytuna swarm optimizationmeta-heuristic algorithm |
| spellingShingle | Jingyu Wang Liangkuan Zhu Bowen Wu Arystan Ryspayev Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization Forests forestry canopy image segmentation symmetric cross-entropy tuna swarm optimization meta-heuristic algorithm |
| title | Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization |
| title_full | Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization |
| title_fullStr | Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization |
| title_full_unstemmed | Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization |
| title_short | Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization |
| title_sort | forestry canopy image segmentation based on improved tuna swarm optimization |
| topic | forestry canopy image segmentation symmetric cross-entropy tuna swarm optimization meta-heuristic algorithm |
| url | https://www.mdpi.com/1999-4907/13/11/1746 |
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