Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty
In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation a...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9628034/ |
| _version_ | 1798022167939514368 |
|---|---|
| author | Liang Zhang Shengtao Lu Canbin Hu Deliang Xiang Tao Liu Yi Su |
| author_facet | Liang Zhang Shengtao Lu Canbin Hu Deliang Xiang Tao Liu Yi Su |
| author_sort | Liang Zhang |
| collection | DOAJ |
| description | In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation algorithm consists of two stages, i.e., fast pixel clustering and superpixel merging. In the clustering stage, we define a new adaptive pixel dissimilarity measure for SAR image and then optimize the DBSCAN strategy, which considers the edge information and can achieve rapid clustering. In the merging stage, based on the initial superpixels, a new superpixel dissimilarity measure is defined, which can merge the small local superpixels into their neighborhood superpixels, making the final superpixel segmentation results compact and regular. Experimental results on two simulated and two real SAR images demonstrate that our method outperforms the state-of-the-art superpixel generation methods in terms of both efficiency and accuracy. The superpixel segmentation accuracy of our method is 5–10% higher and the time cost is 10–40% lower than other methods. Since the superpixel segmentation result can be used as a preprocessing stage for the SAR data interpretation applications, superpixel-based and pixel-based classification results with two real SAR images are also used for comparison, which can validate the advantages of our proposed method. |
| first_indexed | 2024-04-11T17:25:27Z |
| format | Article |
| id | doaj.art-39b4c347944b469bbb6141993db087b4 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T17:25:27Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-39b4c347944b469bbb6141993db087b42022-12-22T04:12:19ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-011580481910.1109/JSTARS.2021.31311879628034Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge PenaltyLiang Zhang0Shengtao Lu1Canbin Hu2Deliang Xiang3https://orcid.org/0000-0003-0152-6621Tao Liu4https://orcid.org/0000-0001-8299-646XYi Su5College of Electronic Science, National University of Defense Technology, Changsha, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaBeijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha, ChinaIn this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation algorithm consists of two stages, i.e., fast pixel clustering and superpixel merging. In the clustering stage, we define a new adaptive pixel dissimilarity measure for SAR image and then optimize the DBSCAN strategy, which considers the edge information and can achieve rapid clustering. In the merging stage, based on the initial superpixels, a new superpixel dissimilarity measure is defined, which can merge the small local superpixels into their neighborhood superpixels, making the final superpixel segmentation results compact and regular. Experimental results on two simulated and two real SAR images demonstrate that our method outperforms the state-of-the-art superpixel generation methods in terms of both efficiency and accuracy. The superpixel segmentation accuracy of our method is 5–10% higher and the time cost is 10–40% lower than other methods. Since the superpixel segmentation result can be used as a preprocessing stage for the SAR data interpretation applications, superpixel-based and pixel-based classification results with two real SAR images are also used for comparison, which can validate the advantages of our proposed method.https://ieeexplore.ieee.org/document/9628034/Clusteringdensity-based spatial clustering of applications with noise (DBSCAN)edge penaltysuperpixel generationsynthetic aperture radar (SAR) image |
| spellingShingle | Liang Zhang Shengtao Lu Canbin Hu Deliang Xiang Tao Liu Yi Su Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Clustering density-based spatial clustering of applications with noise (DBSCAN) edge penalty superpixel generation synthetic aperture radar (SAR) image |
| title | Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty |
| title_full | Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty |
| title_fullStr | Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty |
| title_full_unstemmed | Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty |
| title_short | Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty |
| title_sort | superpixel generation for sar imagery based on fast dbscan clustering with edge penalty |
| topic | Clustering density-based spatial clustering of applications with noise (DBSCAN) edge penalty superpixel generation synthetic aperture radar (SAR) image |
| url | https://ieeexplore.ieee.org/document/9628034/ |
| work_keys_str_mv | AT liangzhang superpixelgenerationforsarimagerybasedonfastdbscanclusteringwithedgepenalty AT shengtaolu superpixelgenerationforsarimagerybasedonfastdbscanclusteringwithedgepenalty AT canbinhu superpixelgenerationforsarimagerybasedonfastdbscanclusteringwithedgepenalty AT deliangxiang superpixelgenerationforsarimagerybasedonfastdbscanclusteringwithedgepenalty AT taoliu superpixelgenerationforsarimagerybasedonfastdbscanclusteringwithedgepenalty AT yisu superpixelgenerationforsarimagerybasedonfastdbscanclusteringwithedgepenalty |