Autonomous Extraction Technology for Aquaculture Ponds in Complex Geological Environments Based on Multispectral Feature Fusion of Medium-Resolution Remote Sensing Imagery
文献类型：期刊论文
| 作者 | Liang, Zunxun3,4; Wang, Fangxiong3,4; Zhu, Jianfeng2,3,4; Li, Peng2,3,4; Xie, Fuding3,4; Zhao, Yifei1,5,6 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 16期号:22页码:4130 |
| 关键词 | aquaculture ponds Sentinel-2 image deep learning multiscale convolution self-attention mechanism complex geological environment |
| DOI | 10.3390/rs16224130 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Coastal aquaculture plays a crucial role in global food security and the economic development of coastal regions, but it also causes environmental degradation in coastal ecosystems. Therefore, the automation, accurate extraction, and monitoring of coastal aquaculture areas are crucial for the scientific management of coastal ecological zones. This study proposes a novel deep learning- and attention-based median adaptive fusion U-Net (MAFU-Net) procedure aimed at precisely extracting individually separable aquaculture ponds (ISAPs) from medium-resolution remote sensing imagery. Initially, this study analyzes the spectral differences between aquaculture ponds and interfering objects such as saltwater fields in four typical aquaculture areas along the coast of Liaoning Province, China. It innovatively introduces a difference index for saltwater field aquaculture zones (DIAS) and integrates this index as a new band into remote sensing imagery to increase the expressiveness of features. A median augmented adaptive fusion module (MEA-FM), which adaptively selects channel receptive fields at various scales, integrates the information between channels, and captures multiscale spatial information to achieve improved extraction accuracy, is subsequently designed. Experimental and comparative results reveal that the proposed MAFU-Net method achieves an F1 score of 90.67% and an intersection over union (IoU) of 83.93% on the CHN-LN4-ISAPS-9 dataset, outperforming advanced methods such as U-Net, DeepLabV3+, SegNet, PSPNet, SKNet, UPS-Net, and SegFormer. This study's results provide accurate data support for the scientific management of aquaculture areas, and the proposed MAFU-Net method provides an effective method for semantic segmentation tasks based on medium-resolution remote sensing images. |
| WOS关键词 | AREA EXTRACTION ; WATER INDEX |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001366582700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210476]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhao, Yifei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Liaoning Normal Univ, Minist Educ, Inst Marine Sustainable Dev, Key Res Base Humanities & Social Sci, Dalian 116029, Peoples R China 3.Liaoning Normal Univ, Sch Geog, Dalian 116029, Peoples R China 4.Liaoning Normal Univ, Liaoning Prov Key Lab Phys Geog & Geomat, Dalian 116029, Peoples R China 5.Nanjing Univ, Collaborat Innovat Ctr South China Sea Studies, Nanjing 210093, Peoples R China 6.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liang, Zunxun,Wang, Fangxiong,Zhu, Jianfeng,et al. Autonomous Extraction Technology for Aquaculture Ponds in Complex Geological Environments Based on Multispectral Feature Fusion of Medium-Resolution Remote Sensing Imagery[J]. REMOTE SENSING,2024,16(22):4130. |
| APA | Liang, Zunxun,Wang, Fangxiong,Zhu, Jianfeng,Li, Peng,Xie, Fuding,&Zhao, Yifei.(2024).Autonomous Extraction Technology for Aquaculture Ponds in Complex Geological Environments Based on Multispectral Feature Fusion of Medium-Resolution Remote Sensing Imagery.REMOTE SENSING,16(22),4130. |
| MLA | Liang, Zunxun,et al."Autonomous Extraction Technology for Aquaculture Ponds in Complex Geological Environments Based on Multispectral Feature Fusion of Medium-Resolution Remote Sensing Imagery".REMOTE SENSING 16.22(2024):4130. |
入库方式： OAI收割
来源：地理科学与资源研究所
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