Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite
Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only descr...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9171999/ |
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| _version_ | 1841556986340048896 |
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| author | Yeqiong Shi Sainan Wang Shuna Zhou M. M. Kamruzzaman |
| author_facet | Yeqiong Shi Sainan Wang Shuna Zhou M. M. Kamruzzaman |
| author_sort | Yeqiong Shi |
| collection | DOAJ |
| description | Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This article mainly studies the forest image recognition system based on CCD and theodolite. In this article, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this article, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target. |
| format | Article |
| id | doaj-art-04ecc6c0c77b45ae9a3d9632031bca62 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-04ecc6c0c77b45ae9a3d9632031bca622025-01-07T00:00:58ZengIEEEIEEE Access2169-35362020-01-01815906715907610.1109/ACCESS.2020.30181809171999Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and TheodoliteYeqiong Shi0Sainan Wang1https://orcid.org/0000-0003-4788-1469Shuna Zhou2https://orcid.org/0000-0001-8417-7185M. M. Kamruzzaman3https://orcid.org/0000-0001-8464-1523Northeast Forestry University, Harbin, ChinaDepartment of Information and Control Engineering, Shenyang Institute of Science and Technology, Shenyang, ChinaManzhouli College, Inner Mongolia University, Manzhouli, ChinaDepartment of Computer and Information Science, Jouf University, Sakaka, Saudi ArabiaForest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This article mainly studies the forest image recognition system based on CCD and theodolite. In this article, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this article, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target.https://ieeexplore.ieee.org/document/9171999/ |
| spellingShingle | Yeqiong Shi Sainan Wang Shuna Zhou M. M. Kamruzzaman Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite IEEE Access |
| title | Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite |
| title_full | Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite |
| title_fullStr | Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite |
| title_full_unstemmed | Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite |
| title_short | Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite |
| title_sort | retracted study on modeling method of forest tree image recognition based on ccd and theodolite |
| url | https://ieeexplore.ieee.org/document/9171999/ |
| work_keys_str_mv | AT yeqiongshi retractedstudyonmodelingmethodofforesttreeimagerecognitionbasedonccdandtheodolite AT sainanwang retractedstudyonmodelingmethodofforesttreeimagerecognitionbasedonccdandtheodolite AT shunazhou retractedstudyonmodelingmethodofforesttreeimagerecognitionbasedonccdandtheodolite AT mmkamruzzaman retractedstudyonmodelingmethodofforesttreeimagerecognitionbasedonccdandtheodolite |