| 迁移学习用于多时相极化SAR影像的水体提取 |
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| 引用本文： | 覃星力,杨杰,李平湘,赵伶俐,孙开敏. 迁移学习用于多时相极化SAR影像的水体提取[J]. 武汉大学学报(信息科学版), 2022, 47(7): 1093-1102. DOI: 10.13203/j.whugis20200121 |
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| 作者姓名： | 覃星力 杨杰 李平湘 赵伶俐 孙开敏 |
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| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室，湖北 武汉，430079 |
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| 基金项目： | 国家自然科学基金61971318国家自然科学基金42001134国家自然科学基金U2033216深圳市科技计划项目JCYJ20200109150833977 |
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| 摘 要： | 基于机器学习分类器的极化合成孔径雷达（synthetic aperture radar, SAR）影像水体提取方法具有较高的可靠性，但其通常依赖于大量的训练样本，利用该方法进行多时相极化SAR影像的水体提取时，在每一景影像上都人工标注足够数量的训练样本是十分困难且耗时的。同时，SAR影像上固有的相干斑点噪声会进一步加剧样本标注的难度。对此，引入迁移学习方法，利用其知识迁移能力将已有的训练样本的类别标签信息迁移至未标注的样本，以降低获取新样本所需的人工代价，提高水体提取的时效性。使用6景极化SAR影像和4种迁移学习方法进行最佳源域影像选取、样本标签迁移和水体提取实验，实验结果表明，迁移学习方法可以准确地将源域影像上的训练样本的标签信息迁移至其他影像，有效减少其他影像进行水体提取需要的人工标注样本的数量，同时能够维持较高的水体提取精度，在洪涝灾害应急响应中具有一定的应用价值。
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| 关 键 词： | 机器学习 极化SAR 水体提取 多时相影像 迁移学习 |
| 收稿时间： | 2020-10-09 |
Water Body Extraction from Multi-temporal Polarimetric SAR Images Based on Transfer Learning |
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| Affiliation: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China2.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China |
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| Abstract: | Objectives Machine learning classifier-based water body extraction methods for polarimetric synthetic aperture radar (PolSAR) images have high reliability but typically require a great number of training samples. Consequently, it is very difficult and time-consuming to manually collect enough training samples when extracting water body from multi-temporal PolSAR images. To this problem, transfer learning is used to reduce the labor cost of querying new samples and improve the timeliness of water body extraction of multi-temporal PolSAR images. Methods Firstly, an optimal source domain image from multi-temporal images is automatically selected according to the distribution difference between images, and the other images are taken as target domain images. Secondly, a group of training samples are queried in the source domain image as source sample set, and the same number of unlabeled samples are randomly sampled from each target domain image as their target domain sample set. And the knowledge of source domain samples is transferred to target domain samples via the transfer learning method. Finally, a random forest classifier-based water body extraction model is trained using the target domain sample set, and is used for the water body extraction of target domain images. Results We have conducted experiments using six PolSAR images and two kinds of transfer learning methods, the results show that: (1) The label transfer accuracy and the water body extraction accuracy are positively correlated. (2) Inductive transfer learning methods achieve higher label transfer accuracy and lower standard deviation. (3) A smaller distribution difference between source and target domain images indicate a greater transferability, and thus a better water body extraction accuracy. (4) The water body extraction results of inductive transfer learning methods have a higher rate of missing detection, while the results of transductive transfer learning methods have a higher rate of false detection. Conclusions In the water body extraction of multi-temporal PolSAR images, the use of transfer learning methods can significantly reduce the number of manually labeled samples needed to construct high-performance classifiers, while maintaining the water body extraction accuracy at a high level. It has great application potentiality in the emergency response of flood disaster. |
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