Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning
Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other...
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PeerJ Inc.
2022-01-01
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| Online Access: | https://peerj.com/articles/12805.pdf |
| _version_ | 1797420377741197312 |
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| author | Caiqiong Wang Lei Zhao Wangfei Zhang Xiyun Mu Shitao Li |
| author_facet | Caiqiong Wang Lei Zhao Wangfei Zhang Xiyun Mu Shitao Li |
| author_sort | Caiqiong Wang |
| collection | DOAJ |
| description | Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get the over-segmentation results of multi-temporal PolSAR images. Secondly, the edge detectors are constructed to extract the edge information of single-temporal PolSAR images and fuse them to get the edge fusion results of multi-temporal PolSAR images. Then, the similarity measurement matrix is constructed based on the over-segmentation results and edge fusion results of multi-temporal PolSAR images. Finally, the normalized cut criterion is used to complete the segmentation of multi-temporal PolSAR images. The performance of the proposed algorithm is verified based on three temporal PolSAR images of Radarsat-2, and compared with the segmentation algorithm of single-temporal PolSAR image. Experimental results revealed the following findings: (1) The proposed algorithm effectively realizes the segmentation of multi-temporal PolSAR images, and achieves ideal segmentation results. Moreover, the segmentation details are excellent, and the region consistency is good. The objects which can’t be distinguished by the single-temporal PolSAR image segmentation algorithm can be segmented. (2) The segmentation accuracy of the proposed multi-temporal algorithm is up to 86.5%, which is significantly higher than that of the single-temporal PolSAR image segmentation algorithm. In general, the segmentation result of proposed algorithm is closer to the optimal segmentation. The optimal segmentation of farmland parcel objects to meet the needs of agricultural production is realized. This lays a good foundation for the further interpretation of multi-temporal PolSAR image. |
| first_indexed | 2024-03-09T07:00:35Z |
| format | Article |
| id | doaj.art-e8490e8d8122499a97e27cb2ed44f632 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:00:35Z |
| publishDate | 2022-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-e8490e8d8122499a97e27cb2ed44f6322023-12-03T09:52:28ZengPeerJ Inc.PeerJ2167-83592022-01-0110e1280510.7717/peerj.12805Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioningCaiqiong Wang0Lei Zhao1Wangfei Zhang2Xiyun Mu3Shitao Li4College of Forestry, Southwest Forestry University, Kunming, Yunnan, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, ChinaCollege of Forestry, Southwest Forestry University, Kunming, Yunnan, ChinaInstitute of Chifeng Forestry Research, Chifeng, Inner Mongolia, ChinaCollege of Geography and Ecotourism, Southwest Forestry University, Kunming, Yunnan, ChinaAbstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get the over-segmentation results of multi-temporal PolSAR images. Secondly, the edge detectors are constructed to extract the edge information of single-temporal PolSAR images and fuse them to get the edge fusion results of multi-temporal PolSAR images. Then, the similarity measurement matrix is constructed based on the over-segmentation results and edge fusion results of multi-temporal PolSAR images. Finally, the normalized cut criterion is used to complete the segmentation of multi-temporal PolSAR images. The performance of the proposed algorithm is verified based on three temporal PolSAR images of Radarsat-2, and compared with the segmentation algorithm of single-temporal PolSAR image. Experimental results revealed the following findings: (1) The proposed algorithm effectively realizes the segmentation of multi-temporal PolSAR images, and achieves ideal segmentation results. Moreover, the segmentation details are excellent, and the region consistency is good. The objects which can’t be distinguished by the single-temporal PolSAR image segmentation algorithm can be segmented. (2) The segmentation accuracy of the proposed multi-temporal algorithm is up to 86.5%, which is significantly higher than that of the single-temporal PolSAR image segmentation algorithm. In general, the segmentation result of proposed algorithm is closer to the optimal segmentation. The optimal segmentation of farmland parcel objects to meet the needs of agricultural production is realized. This lays a good foundation for the further interpretation of multi-temporal PolSAR image.https://peerj.com/articles/12805.pdfMulti-temporalPolarimetric SARSpectral graph partitioningMean-shiftNormalized cut |
| spellingShingle | Caiqiong Wang Lei Zhao Wangfei Zhang Xiyun Mu Shitao Li Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning PeerJ Multi-temporal Polarimetric SAR Spectral graph partitioning Mean-shift Normalized cut |
| title | Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning |
| title_full | Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning |
| title_fullStr | Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning |
| title_full_unstemmed | Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning |
| title_short | Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning |
| title_sort | segmentation of multi temporal polarimetric sar data based on mean shift and spectral graph partitioning |
| topic | Multi-temporal Polarimetric SAR Spectral graph partitioning Mean-shift Normalized cut |
| url | https://peerj.com/articles/12805.pdf |
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