标记数据集用于训练SAR图像的除过器
Rubén Darío Vásquez-Salazar1, Ahmed Alejandro Cardona-Mesa2, Luis Gómez3
1Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, Medellín, 48th Av, 7-151, Colombia.
Data in brief
|February 6, 2024
概括
这项研究引入了一套新的数据集,用于训练人工智能 (AI) 模型进行合成孔径雷达 (SAR) 除. 它使用实际的SAR图像,为遥感中的监督学习任务提供了宝贵的资源.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 监督学习模型需要标记的数据集,通常是输入-输出对.
- 对于像SAR脱斑这样的图像处理任务,需要一个杂的图像及其相应的否定地面真相.
- 现有的SAR抹黑方法通常依赖于合成损坏的图像,因为缺乏现实世界的地面真相.
研究的目的:
- 通过使用真实的Sentinel-1SAR图像来呈现SAR除斑的新数据集.
- 为了克服当前SAR脱落数据集中合成噪声的限制.
- 促进人工智能和深度学习模型的开发和培训,用于SAR图像增强.
主要方法:
- 利用了来自同一地理区域的Sentinel-1SAR图像,这些图像是在不同的时间拍摄的.
- 处理并合并多个SAR图像以创建一个单一的地面真相图像.
- 将所有SAR图像 (噪音和地面真相) 分成1600个小图像,每个图像为512x512像素.
- 将数据集组织成3000张用于培训的图像和200张用于验证的图像,带有标签的文件.
主要成果:
- 创建了一个包含3200张图像 (1600张杂图像,1600张实地图像) 的数据集.
- 数据集结构为监督学习,提供真实的SAR数据对.
- 数据集被分为用于模型开发的培训和验证集.
结论:
- 拟议的数据集为训练SAR脱落的AI模型提供了一个现实的基础.
- 本资源解决了在开发先进的SAR图像处理技术时对真实数据的需求.
- 预计这一数据集的可用性将推动人工智能驱动的SAR图像分析研究.
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