Improved fast mean shift algorithm for remote sensing image segmentation
Image segmentation plays a crucial role in object-based remote sensing information extraction. This study improves the existing mean shift (MS) algorithm for segmenting high resolution remote sensing imagery by adopting two strategies. First, a pixel-based, fixed bandwidth and weighted MS algorithm...
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| Published in | IET image processing Vol. 9; no. 5; pp. 389 - 394 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
The Institution of Engineering and Technology
01.05.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-9659 1751-9667 1751-9667 |
| DOI | 10.1049/iet-ipr.2014.0393 |
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| Abstract | Image segmentation plays a crucial role in object-based remote sensing information extraction. This study improves the existing mean shift (MS) algorithm for segmenting high resolution remote sensing imagery by adopting two strategies. First, a pixel-based, fixed bandwidth and weighted MS algorithm is applied to cluster the image. In this process, the space bandwidth is selected according to the resolution of remote sensing images, and the range bandwidths of each band are calculated based on grey feature and the plug-in rule. Gaussian kernels are used for clustering. Second, a region-based MS algorithm is applied to globally merge modes which are obtained in the first step. The spatial and range bandwidths are adaptively adjusted based on the clustering result of the first step. Experimental results with two Quickbird images show that the improved algorithm is superior to the typical MS algorithm, producing high precision and requiring less operation time. |
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| AbstractList | Image segmentation plays a crucial role in object‐based remote sensing information extraction. This study improves the existing mean shift (MS) algorithm for segmenting high resolution remote sensing imagery by adopting two strategies. First, a pixel‐based, fixed bandwidth and weighted MS algorithm is applied to cluster the image. In this process, the space bandwidth is selected according to the resolution of remote sensing images, and the range bandwidths of each band are calculated based on grey feature and the plug‐in rule. Gaussian kernels are used for clustering. Second, a region‐based MS algorithm is applied to globally merge modes which are obtained in the first step. The spatial and range bandwidths are adaptively adjusted based on the clustering result of the first step. Experimental results with two Quickbird images show that the improved algorithm is superior to the typical MS algorithm, producing high precision and requiring less operation time. |
| Author | Fan, Chong Zhou, Jia-Xiang Li, Zhi-Wei |
| Author_xml | – sequence: 1 givenname: Jia-Xiang surname: Zhou fullname: Zhou, Jia-Xiang organization: School of Geosciences and Info-physics, Central South University, Changsha Hunan 410083, People's Republic of China – sequence: 2 givenname: Zhi-Wei surname: Li fullname: Li, Zhi-Wei organization: School of Geosciences and Info-physics, Central South University, Changsha Hunan 410083, People's Republic of China – sequence: 3 givenname: Chong surname: Fan fullname: Fan, Chong email: fanchong@126.com organization: School of Geosciences and Info-physics, Central South University, Changsha Hunan 410083, People's Republic of China |
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| CitedBy_id | crossref_primary_10_1016_j_geoderma_2021_115092 crossref_primary_10_7454_mst_v25i1_3789 crossref_primary_10_1155_2023_9979431 crossref_primary_10_1088_1361_6501_abfbfd crossref_primary_10_1142_S0218126621500274 |
| Cites_doi | 10.1016/j.patrec.2006.10.001 10.1007/978-3-540-24671-8_19 10.1109/34.1000236 10.1109/TPAMI.2003.1195991 10.1109/LGRS.2007.905121 10.1080/01431160512331316838 10.1109/ICCV.2003.1238382 10.1080/18756891.2012.670521 10.3390/rs5073259 10.1109/TPAMI.2003.1240123 10.1109/CVPR.2006.44 10.1109/ICIP.2002.1038964 10.1109/ICCV.1999.790416 10.1016/j.envsoft.2008.11.014 10.1109/LGRS.2004.837009 10.1109/CVPR.2003.1211338 10.1016/j.imavis.2008.09.008 10.1016/j.patcog.2011.12.012 |
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| Keywords | remote sensing image segmentation fixed bandwidth MS algorithm plug-in rule pixel-based MS algorithm remote sensing geophysical image processing Gaussian kernels image clustering pattern clustering weighted MS algorithm image segmentation space bandwidth region-based MS algorithm Quickbird images grey feature fast mean shift algorithm |
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| SubjectTerms | Algorithms Bandwidth Clustering fast mean shift algorithm fixed bandwidth MS algorithm Gaussian Gaussian kernels geophysical image processing grey feature image clustering Image segmentation pattern clustering pixel‐based MS algorithm plug‐in rule Quickbird images region‐based MS algorithm Remote sensing remote sensing image segmentation space bandwidth Strategy weighted MS algorithm |
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| Title | Improved fast mean shift algorithm for remote sensing image segmentation |
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