Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery
Airborne SAR is an important data source for crop mapping and has important applications in agricultural monitoring and food safety. However, the incidence-angle effects of airborne SAR imagery decrease the crop mapping accuracy. An active pairwise constraint learning method (APCL) is proposed for c...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/23/6073 |
| _version_ | 1797462272026607616 |
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| author | Xingli Qin Lingli Zhao Jie Yang Pingxiang Li Bingfang Wu Kaimin Sun Yubin Xu |
| author_facet | Xingli Qin Lingli Zhao Jie Yang Pingxiang Li Bingfang Wu Kaimin Sun Yubin Xu |
| author_sort | Xingli Qin |
| collection | DOAJ |
| description | Airborne SAR is an important data source for crop mapping and has important applications in agricultural monitoring and food safety. However, the incidence-angle effects of airborne SAR imagery decrease the crop mapping accuracy. An active pairwise constraint learning method (APCL) is proposed for constrained time-series clustering to address this problem. APCL constructs two types of instance-level pairwise constraints based on the incidence angles of the samples and a non-iterative batch-mode active selection scheme: the must-link constraint, which links two objects of the same crop type with large differences in backscattering coefficients and the shapes of time-series curves; the cannot-link constraint, which links two objects of different crop types with only small differences in the values of backscattering coefficients. Experiments were conducted using 12 time-series images with incidence angles ranging from 21.2° to 64.3°, and the experimental results prove the effectiveness of APCL in improving crop mapping accuracy. More specifically, when using dynamic time warping (DTW) as the similarity measure, the kappa coefficient obtained by APCL was increased by 9.5%, 8.7%, and 5.2% compared to the results of the three other methods. It provides a new solution for reducing the incidence-angle effects in the crop mapping of airborne SAR time-series images. |
| first_indexed | 2024-03-09T17:34:09Z |
| format | Article |
| id | doaj.art-c689cc4bd32f490aa2e9d79ba64691ee |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:34:09Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c689cc4bd32f490aa2e9d79ba64691ee2023-11-24T12:05:22ZengMDPI AGRemote Sensing2072-42922022-11-011423607310.3390/rs14236073Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR ImageryXingli Qin0Lingli Zhao1Jie Yang2Pingxiang Li3Bingfang Wu4Kaimin Sun5Yubin Xu6State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaChina Academy of Civil Aviation Science and Technology, Beijing 100028, ChinaAirborne SAR is an important data source for crop mapping and has important applications in agricultural monitoring and food safety. However, the incidence-angle effects of airborne SAR imagery decrease the crop mapping accuracy. An active pairwise constraint learning method (APCL) is proposed for constrained time-series clustering to address this problem. APCL constructs two types of instance-level pairwise constraints based on the incidence angles of the samples and a non-iterative batch-mode active selection scheme: the must-link constraint, which links two objects of the same crop type with large differences in backscattering coefficients and the shapes of time-series curves; the cannot-link constraint, which links two objects of different crop types with only small differences in the values of backscattering coefficients. Experiments were conducted using 12 time-series images with incidence angles ranging from 21.2° to 64.3°, and the experimental results prove the effectiveness of APCL in improving crop mapping accuracy. More specifically, when using dynamic time warping (DTW) as the similarity measure, the kappa coefficient obtained by APCL was increased by 9.5%, 8.7%, and 5.2% compared to the results of the three other methods. It provides a new solution for reducing the incidence-angle effects in the crop mapping of airborne SAR time-series images.https://www.mdpi.com/2072-4292/14/23/6073synthetic aperture radar (SAR)crop mappingtime-series imagesconstrained clusteringactive constraint learning |
| spellingShingle | Xingli Qin Lingli Zhao Jie Yang Pingxiang Li Bingfang Wu Kaimin Sun Yubin Xu Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery Remote Sensing synthetic aperture radar (SAR) crop mapping time-series images constrained clustering active constraint learning |
| title | Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery |
| title_full | Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery |
| title_fullStr | Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery |
| title_full_unstemmed | Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery |
| title_short | Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery |
| title_sort | active pairwise constraint learning in constrained time series clustering for crop mapping from airborne sar imagery |
| topic | synthetic aperture radar (SAR) crop mapping time-series images constrained clustering active constraint learning |
| url | https://www.mdpi.com/2072-4292/14/23/6073 |
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