Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information
The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2385 |
| _version_ | 1811279100620308480 |
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| author | Muqing Li Luping Xu Shan Gao Na Xu Bo Yan |
| author_facet | Muqing Li Luping Xu Shan Gao Na Xu Bo Yan |
| author_sort | Muqing Li |
| collection | DOAJ |
| description | The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, this paper proposes a clustering algorithm based on spatial information to improve the anti-noise and accuracy of image segmentation. Firstly, the image is roughly clustered using the improved Lévy grey wolf optimization algorithm (LGWO) to obtain the initial clustering center. Secondly, the neighborhood and non-neighborhood information around the pixel is added into the target function as spatial information, the weight between the pixel information and non-neighborhood spatial information is adjusted by information entropy, and the traditional Euclidean distance is replaced by the improved distance measure. Finally, the objective function is optimized by the gradient descent method to segment the image correctly. |
| first_indexed | 2024-04-13T00:48:31Z |
| format | Article |
| id | doaj.art-532dd0fb7fc5400eba11929b5507b9ac |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:48:31Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-532dd0fb7fc5400eba11929b5507b9ac2022-12-22T03:09:56ZengMDPI AGSensors1424-82202019-05-011910238510.3390/s19102385s19102385Adaptive Segmentation of Remote Sensing Images Based on Global Spatial InformationMuqing Li0Luping Xu1Shan Gao2Na Xu3Bo Yan4School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, ChinaSchool of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, ChinaResearch Institute of Vibration Engineering, ZhengZhou University, 100 Kexue Avenue of Gaoxin Section, ZhengZhou 450001, ChinaSchool of Life Sciences and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, ChinaSchool of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, ChinaThe problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, this paper proposes a clustering algorithm based on spatial information to improve the anti-noise and accuracy of image segmentation. Firstly, the image is roughly clustered using the improved Lévy grey wolf optimization algorithm (LGWO) to obtain the initial clustering center. Secondly, the neighborhood and non-neighborhood information around the pixel is added into the target function as spatial information, the weight between the pixel information and non-neighborhood spatial information is adjusted by information entropy, and the traditional Euclidean distance is replaced by the improved distance measure. Finally, the objective function is optimized by the gradient descent method to segment the image correctly.https://www.mdpi.com/1424-8220/19/10/2385image segmentationglobal spatial informationadaptive parametersstrong denoising |
| spellingShingle | Muqing Li Luping Xu Shan Gao Na Xu Bo Yan Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information Sensors image segmentation global spatial information adaptive parameters strong denoising |
| title | Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information |
| title_full | Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information |
| title_fullStr | Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information |
| title_full_unstemmed | Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information |
| title_short | Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information |
| title_sort | adaptive segmentation of remote sensing images based on global spatial information |
| topic | image segmentation global spatial information adaptive parameters strong denoising |
| url | https://www.mdpi.com/1424-8220/19/10/2385 |
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