Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation
The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characterist...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/24/4115 |
| _version_ | 1797544510055514112 |
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| author | Xiaoli Li Jinsong Chen Longlong Zhao Shanxin Guo Luyi Sun Xuemei Zhao |
| author_facet | Xiaoli Li Jinsong Chen Longlong Zhao Shanxin Guo Luyi Sun Xuemei Zhao |
| author_sort | Xiaoli Li |
| collection | DOAJ |
| description | The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics. |
| first_indexed | 2024-03-10T14:01:33Z |
| format | Article |
| id | doaj.art-f08ac58507794738b5ed143bb571d3ab |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:01:33Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f08ac58507794738b5ed143bb571d3ab2023-11-21T01:06:49ZengMDPI AGRemote Sensing2072-42922020-12-011224411510.3390/rs12244115Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image SegmentationXiaoli Li0Jinsong Chen1Longlong Zhao2Shanxin Guo3Luyi Sun4Xuemei Zhao5Center for Geospatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaCenter for Geospatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaCenter for Geospatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaCenter for Geospatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaCenter for Geospatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSchool of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, ChinaThe spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics.https://www.mdpi.com/2072-4292/12/24/4115adaptive distance-weightedVoronoi tessellationMarkov Random Field (MRF)Kullback–Leibler (KL) entropyfuzzy clusteringremote sensing image segmentation |
| spellingShingle | Xiaoli Li Jinsong Chen Longlong Zhao Shanxin Guo Luyi Sun Xuemei Zhao Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation Remote Sensing adaptive distance-weighted Voronoi tessellation Markov Random Field (MRF) Kullback–Leibler (KL) entropy fuzzy clustering remote sensing image segmentation |
| title | Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation |
| title_full | Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation |
| title_fullStr | Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation |
| title_full_unstemmed | Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation |
| title_short | Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation |
| title_sort | adaptive distance weighted voronoi tessellation for remote sensing image segmentation |
| topic | adaptive distance-weighted Voronoi tessellation Markov Random Field (MRF) Kullback–Leibler (KL) entropy fuzzy clustering remote sensing image segmentation |
| url | https://www.mdpi.com/2072-4292/12/24/4115 |
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