Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach
In general, changes in the multitemporal synthetic aperture radar (SAR) images are detected by classifying the SAR ratio images into the changed and unchanged classes. However, multitemporal SAR images have either increase or decrease in the backscattering values. Therefore, the changed areas can be...
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8713850/ |
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| author | Shuang Liang Hao Li Yun Zhu Maoguo Gong |
| author_facet | Shuang Liang Hao Li Yun Zhu Maoguo Gong |
| author_sort | Shuang Liang |
| collection | DOAJ |
| description | In general, changes in the multitemporal synthetic aperture radar (SAR) images are detected by classifying the SAR ratio images into the changed and unchanged classes. However, multitemporal SAR images have either increase or decrease in the backscattering values. Therefore, the changed areas can be further classified into positive and negative changed classes. This paper presents an unsupervised change detection approach for detecting the positive and negative changes based on a multi-objective evolutionary algorithm. In this paper, the widely adopted mean-ratio and log-ratio operators are extended to generate SAR ratio images for distinguishing the positive and negative changes. In order to reduce the corruption of speckle noise present in the multitemporal SAR images, a fuzzy cluster validity index is established to exploit local spatial and gray level information. Then the objective functions are simultaneously optimized by a multi-objective evolutionary algorithm. The experimental results on two simulated data sets and three real SAR data sets confirm the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-eedff73e4a674c3c9264b6449546e88c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eedff73e4a674c3c9264b6449546e88c2025-01-09T00:00:40ZengIEEEIEEE Access2169-35362019-01-017636386364910.1109/ACCESS.2019.29168998713850Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective ApproachShuang Liang0Hao Li1https://orcid.org/0000-0002-6294-6761Yun Zhu2Maoguo Gong3School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, Xidian University, Xi’an, ChinaSchool of Computer Science, Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, Xidian University, Xi’an, ChinaIn general, changes in the multitemporal synthetic aperture radar (SAR) images are detected by classifying the SAR ratio images into the changed and unchanged classes. However, multitemporal SAR images have either increase or decrease in the backscattering values. Therefore, the changed areas can be further classified into positive and negative changed classes. This paper presents an unsupervised change detection approach for detecting the positive and negative changes based on a multi-objective evolutionary algorithm. In this paper, the widely adopted mean-ratio and log-ratio operators are extended to generate SAR ratio images for distinguishing the positive and negative changes. In order to reduce the corruption of speckle noise present in the multitemporal SAR images, a fuzzy cluster validity index is established to exploit local spatial and gray level information. Then the objective functions are simultaneously optimized by a multi-objective evolutionary algorithm. The experimental results on two simulated data sets and three real SAR data sets confirm the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/8713850/Evolutionary algorithmimage change detectionmulti-objective optimizationsynthetic aperture radar |
| spellingShingle | Shuang Liang Hao Li Yun Zhu Maoguo Gong Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach IEEE Access Evolutionary algorithm image change detection multi-objective optimization synthetic aperture radar |
| title | Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach |
| title_full | Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach |
| title_fullStr | Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach |
| title_full_unstemmed | Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach |
| title_short | Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach |
| title_sort | detecting positive and negative changes from sar images by an evolutionary multi objective approach |
| topic | Evolutionary algorithm image change detection multi-objective optimization synthetic aperture radar |
| url | https://ieeexplore.ieee.org/document/8713850/ |
| work_keys_str_mv | AT shuangliang detectingpositiveandnegativechangesfromsarimagesbyanevolutionarymultiobjectiveapproach AT haoli detectingpositiveandnegativechangesfromsarimagesbyanevolutionarymultiobjectiveapproach AT yunzhu detectingpositiveandnegativechangesfromsarimagesbyanevolutionarymultiobjectiveapproach AT maoguogong detectingpositiveandnegativechangesfromsarimagesbyanevolutionarymultiobjectiveapproach |