Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images
Compact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment...
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Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2319939 |
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| author | Xianyu Guo Junjun Yin Jian Yang |
| author_facet | Xianyu Guo Junjun Yin Jian Yang |
| author_sort | Xianyu Guo |
| collection | DOAJ |
| description | Compact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning. |
| format | Article |
| id | doaj-art-9631c048277f47fd832947458126e4c2 |
| institution | OA Journals |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-9631c048277f47fd832947458126e4c22025-08-20T02:31:26ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2024.2319939Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR imagesXianyu Guo0Junjun Yin1Jian Yang2School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing, ChinaCompact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning.https://www.tandfonline.com/doi/10.1080/15481603.2024.2319939Fine classificationcropstransfer learningcompact polarimetrySynthetic aperture radar (SAR) |
| spellingShingle | Xianyu Guo Junjun Yin Jian Yang Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images GIScience & Remote Sensing Fine classification crops transfer learning compact polarimetry Synthetic aperture radar (SAR) |
| title | Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images |
| title_full | Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images |
| title_fullStr | Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images |
| title_full_unstemmed | Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images |
| title_short | Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images |
| title_sort | fine classification of crops based on an inductive transfer learning method with compact polarimetric sar images |
| topic | Fine classification crops transfer learning compact polarimetry Synthetic aperture radar (SAR) |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2024.2319939 |
| work_keys_str_mv | AT xianyuguo fineclassificationofcropsbasedonaninductivetransferlearningmethodwithcompactpolarimetricsarimages AT junjunyin fineclassificationofcropsbasedonaninductivetransferlearningmethodwithcompactpolarimetricsarimages AT jianyang fineclassificationofcropsbasedonaninductivetransferlearningmethodwithcompactpolarimetricsarimages |