A Chlorophyll-a Concentration Inversion Model Based on Adaptive Fine-Tuning Transfer Learning Method and Self-Attention Mechanism
文献类型：期刊论文
| 作者 | Cui, Jianyong2; Hou, Shuhang2; Guo, Jie1; Xu, Mingming2; Sheng, Hui2; Liu, Shanwei2; Yasir, Muhammad2; Zhang, Ying3 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:25357-25373 |
| 关键词 | Adaptation models Water quality Biological system modeling Monitoring Data models Sea measurements Optimization Remote sensing Chlorophyll-a (Chl-a) coastal waters crested porcupine optimizer (CPO) transformer transfer learning transformer transfer learning |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3611596 |
| 通讯作者 | Cui, Jianyong(cui_jianyong@upc.edu.cn) |
| 英文摘要 | Chlorophyll-a (Chl-a) is essential for assessing water quality and aquatic ecosystems. Traditional in situ monitoring suffers from sparse stations and limited samples, and machine learning often overfits under such conditions. Transfer learning offers a solution by leveraging pretrained knowledge, but domain discrepancies remain a major challenge. To improve cross-domain Chl-a inversion under few-shot settings, this study proposes GSA-ICPO-Former, which integrates a Transformer architecture, an improved crested porcupine optimizer (ICPO), and a gradient sensitivity-based adaptive (GSA) fine-tuning method. The Transformer captures global spectral dependencies through self-attention; ICPO enhances hyperparameter optimization and prevents overfitting; GSA adjusts model parameters to adapt source-domain knowledge to target-domain features. After ICPO optimization, the model's R-2 in the source domain improved from 0.73 to 0.84, and RMSE decreased from 2.39 to 1.86 mu g/L, representing improvements of 15.07% and 22.18%, respectively. In the target domain, compared with ICPO-Former, GSA-ICPO-Former raised R-2 from 0.54 to 0.79 and reduced RMSE by 32.97%, demonstrating enhanced cross-domain adaptability. Ablation studies confirmed the effectiveness of each optimization component. The model also showed robust performance across different coastal environments, including both nearshore (Tangdao Bay) and open-sea (Rongcheng) regions. It accurately tracked Chl-a distribution with limited samples and showed promise in dynamic algal bloom monitoring. Overall, the proposed model effectively bridges domain gaps and maintains high prediction accuracy in low-sample, cross-regional scenarios, providing a practical tool for satellite-based coastal water quality monitoring. |
| WOS关键词 | INDEX |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001591683700007 |
| 资助机构 | NSFC Shiptime Sharing |
| 源URL | [http://ir.yic.ac.cn/handle/133337/41471]  |
| 专题 | 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 通讯作者 | Cui, Jianyong |
| 作者单位 | 1.Chinese Acad Sci, Shandong Key Lab Coastal Environm Proc, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 2.China Univ Petr East China, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China 3.Nanjing Univ Sci & Technol, Sch Microelect, Sch Integrated Circuits, Nanjing 210094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cui, Jianyong,Hou, Shuhang,Guo, Jie,et al. A Chlorophyll-a Concentration Inversion Model Based on Adaptive Fine-Tuning Transfer Learning Method and Self-Attention Mechanism[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:25357-25373. |
| APA | Cui, Jianyong.,Hou, Shuhang.,Guo, Jie.,Xu, Mingming.,Sheng, Hui.,...&Zhang, Ying.(2025).A Chlorophyll-a Concentration Inversion Model Based on Adaptive Fine-Tuning Transfer Learning Method and Self-Attention Mechanism.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,25357-25373. |
| MLA | Cui, Jianyong,et al."A Chlorophyll-a Concentration Inversion Model Based on Adaptive Fine-Tuning Transfer Learning Method and Self-Attention Mechanism".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):25357-25373. |
入库方式： OAI收割
来源：烟台海岸带研究所
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