Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series
The classification of image time series has potential significance in the field of land-cover analysis with the increasing number of remote sensing images. The key problem of the classification of image time series is how to transfer the already available knowledge on the source domain to the target...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9613766/ |
| _version_ | 1818753701750243328 |
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| author | Hao Li Jianzhao Li Yue Zhao Maoguo Gong Yujing Zhang Tongfei Liu |
| author_facet | Hao Li Jianzhao Li Yue Zhao Maoguo Gong Yujing Zhang Tongfei Liu |
| author_sort | Hao Li |
| collection | DOAJ |
| description | The classification of image time series has potential significance in the field of land-cover analysis with the increasing number of remote sensing images. The key problem of the classification of image time series is how to transfer the already available knowledge on the source domain to the target domain. Nevertheless, most of the existing methods do not consider the impact of different sample costs on the classifier during transferring. In addition, it is very difficult to collect reliable labeled samples with changed or unchanged categories between the source domain and the target domain in the case of a large number of training samples. In order to alleviate the above problems, we propose a cost-sensitive self-paced learning (CSSPL) framework with adaptive regularization for the classification of image time series in this article. Considering that the costs of different samples cannot be completely equal to the classifier, different cost values are assigned to each type of error first, then we minimize the total cost to give the change detection classifier a preference on the unchanged class, aiming to reduce wrong label propagation from the source to the target image. Besides, an adaptive mixture weight regularizer is designed to automatically assign sample weight based on the loss value of the dataset, which enables more reliable sample weights to be selected for training. Experimental results show that the proposed algorithm provides a set of reliable samples for the training of classifier and achieves a promising improvement on classification accuracy. |
| first_indexed | 2024-12-18T05:11:33Z |
| format | Article |
| id | doaj.art-714a97b0b02343f59ea68bfbad0a11f7 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-18T05:11:33Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-714a97b0b02343f59ea68bfbad0a11f72022-12-21T21:19:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-0114117131172710.1109/JSTARS.2021.31277549613766Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time SeriesHao Li0https://orcid.org/0000-0002-6294-6761Jianzhao Li1https://orcid.org/0000-0002-1524-1363Yue Zhao2Maoguo Gong3https://orcid.org/0000-0002-0415-8556Yujing Zhang4Tongfei Liu5https://orcid.org/0000-0003-1394-4724School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of education of China, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of education of China, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of education of China, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of education of China, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of education of China, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of education of China, Xidian University, Xi’an, ChinaThe classification of image time series has potential significance in the field of land-cover analysis with the increasing number of remote sensing images. The key problem of the classification of image time series is how to transfer the already available knowledge on the source domain to the target domain. Nevertheless, most of the existing methods do not consider the impact of different sample costs on the classifier during transferring. In addition, it is very difficult to collect reliable labeled samples with changed or unchanged categories between the source domain and the target domain in the case of a large number of training samples. In order to alleviate the above problems, we propose a cost-sensitive self-paced learning (CSSPL) framework with adaptive regularization for the classification of image time series in this article. Considering that the costs of different samples cannot be completely equal to the classifier, different cost values are assigned to each type of error first, then we minimize the total cost to give the change detection classifier a preference on the unchanged class, aiming to reduce wrong label propagation from the source to the target image. Besides, an adaptive mixture weight regularizer is designed to automatically assign sample weight based on the loss value of the dataset, which enables more reliable sample weights to be selected for training. Experimental results show that the proposed algorithm provides a set of reliable samples for the training of classifier and achieves a promising improvement on classification accuracy.https://ieeexplore.ieee.org/document/9613766/Classificationcost-sensitiveself-paced learningtime series |
| spellingShingle | Hao Li Jianzhao Li Yue Zhao Maoguo Gong Yujing Zhang Tongfei Liu Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification cost-sensitive self-paced learning time series |
| title | Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series |
| title_full | Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series |
| title_fullStr | Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series |
| title_full_unstemmed | Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series |
| title_short | Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series |
| title_sort | cost sensitive self paced learning with adaptive regularization for classification of image time series |
| topic | Classification cost-sensitive self-paced learning time series |
| url | https://ieeexplore.ieee.org/document/9613766/ |
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