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一种高分辨率SAR图像变化检测数据集制作方法
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Abstract:
针对可用于变化检测模型训练的高分辨率SAR图像数据不足、SAR图像处理与样本标注复杂的问题，本文开展SAR图像变化检测数据集制作方法研究，简化SAR图像处理与样本标注流程，对多幅高分三号SAR影像依次进行预处理、精配准、样本标注与数据增强等操作，得到一套高分辨率SAR图像变化检测数据集，为深度学习变化检测网络的训练与测试提供数据基础。通过多种网络模型训练的实验测试与结果分析，验证了该数据集能够有效支持多场景变化检测任务，对于不同变化检测模型训练精度与效率的提升具有较好的参考价值。
Aiming at the problems of insufficient high-resolution SAR image data that can be used for change detection model training and the complexity of SAR image processing and sample labeling, this pa-per carried out research on the production method of SAR image change detection data set to sim-plify the SAR image processing and sample labeling process. A set of high-resolution SAR image change detection data set is obtained by preprocessing, fine registration, sample labeling and data enhancement of several high-resolution third-level SAR images successively, which provides data basis for the training and testing of deep learning change detection network. Through the experi-mental test and analysis of STANet, DSAMNet and SNUNet network model training, the validity of the SAR image change detection data set obtained by the data set making method proposed in this paper is verified in the multi-scene change detection task.
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