Difference-Focusing Fusion Decision Method: An Ensemble Learning Framework and Its Application in Improving Deep Learning Sea-Land Segmentation for Waterline Extraction in Synthetic Aperture Radar Imagery
文献类型：期刊论文
| 作者 | Zheng, Gang2,3,4,5; Zhou, Yinfei1,4,5,7; Liu, Bin8; Zhou, Lizhang4; Jiang, Han4,6; Wan, Xuanwei4; Chen, Peng4 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 62页码:14 |
| 关键词 | Image resolution Image segmentation Radar polarimetry Training Oceanography Oceans Synthetic aperture radar Deep learning ensemble learning framework segmentation synthetic aperture radar (SAR) waterline |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2024.3446317 |
| 通讯作者 | Zheng, Gang(zhenggang@sio.org.cn) |
| 英文摘要 | Waterline extraction from synthetic aperture radar (SAR) images can be transformed into a sea-land segmentation task. However, two aspects of deep learning sea-land segmentation have been ignored in the literature: 1) deep learning models are commonly built using whole images rather than focusing on their sea-land transition parts and 2) a higher resolution input may not render a better segmentation result under the constraint of a fixed-size receptive field. Our investigation on the aspects indicates that focusing the modeling process on the sea-land transition parts can benefit the waterline extraction, and the highest resolution may not be the best choice for all pixels. We proposed masked soft intersection over union (MSIoU) loss and the difference-focusing fusion decision (DFFD) ensemble learning method. MSIoU loss incorporates the mask of the transition parts to focus the modeling process on the transition parts. The DFFD ensemble learning method imitates manual labeling and can avoid selecting the resolution of input images. The DFFD ensemble model's member models segment an image's sea and land areas at different resolutions. Then, its fusion model further recognizes the pixels where the member models inconsistently predict sea-land types. We applied the DFFD ensemble model to 10-m-resolution SAR images of the test set. Compared to the traditional single-resolution model, the DFFD ensemble model with MSIoU loss achieved a 10.32-12.58-m accuracy in waterline extraction with a 2.08-2.78-m error reduction in the study area. Moreover, the DFFD framework is independent of data and model choice and can be readily modified for other segmentation tasks. |
| WOS关键词 | SHORELINE EXTRACTION ; SATELLITE IMAGES ; COASTLINE ; CALIBRATION ; NETWORK |
| 资助项目 | Zhejiang Provincial Natural Science Foundation of China[LR21D060002] ; National Natural Science Foundation of China[42306216] ; Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources[XRJH2304] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001303543600019 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/198606]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Zheng, Gang |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 3.Hohai Univ, Coll Oceanog, Nanjing 210098, Peoples R China 4.Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China 5.Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China 6.PIESAT Informat Technol Co Ltd, Beijing 100195, Peoples R China 7.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 8.Shanghai Ocean Univ, Coll Oceanog & Ecol Sci, Shanghai 201306, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Gang,Zhou, Yinfei,Liu, Bin,et al. Difference-Focusing Fusion Decision Method: An Ensemble Learning Framework and Its Application in Improving Deep Learning Sea-Land Segmentation for Waterline Extraction in Synthetic Aperture Radar Imagery[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:14. |
| APA | Zheng, Gang.,Zhou, Yinfei.,Liu, Bin.,Zhou, Lizhang.,Jiang, Han.,...&Chen, Peng.(2024).Difference-Focusing Fusion Decision Method: An Ensemble Learning Framework and Its Application in Improving Deep Learning Sea-Land Segmentation for Waterline Extraction in Synthetic Aperture Radar Imagery.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,14. |
| MLA | Zheng, Gang,et al."Difference-Focusing Fusion Decision Method: An Ensemble Learning Framework and Its Application in Improving Deep Learning Sea-Land Segmentation for Waterline Extraction in Synthetic Aperture Radar Imagery".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):14. |
入库方式： OAI收割
来源：海洋研究所
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