半监督对比学习用于遥感:识别南中安第斯山脉中古城市化
Jiachen Xu1, Junlin Guo2, James Zimmer-Dauphinee3
1School of Engineering, Vanderbilt University, Nashville, USA.
International journal of remote sensing
|March 25, 2024
概括
这项研究引入了一种半监督学习框架,以改善卫星图像中考古遗址的检测. 这种新的方法有效地解决了数据不平衡问题,提高了识别古代结构的准确性.
科学领域:
- 数字考古学 数字考古学
- 机器学习在文化遗产中的应用
- 远程传感用于考古勘探.
背景情况:
- 传统的考古调查面临规模和采样效率的局限性.
- 手动分析卫星和空中图像提供了更广泛的观点,但是劳动密集型和主观的.
- 现有的自我监督学习方法在考古特征检测中常见的高度不平衡的数据集中扎.
研究的目的:
- 开发一个可扩展的框架,从卫星图像中检测考古特征.
- 解决大型不平衡数据集中罕见的考古特征的"长尾问题".
- 通过机器学习提高考古遗址识别的效率和准确性.
主要方法:
- 提出了一种新的半监督学习框架,整合了标记和未标记的卫星图像.
- 利用固有的数据不平衡来生成伪负对来进行对比学习.
- 将该方法应用于95,358个未标记和5,830个标记图像的数据集,以检测古代建筑物.
主要成果:
- 半监督的对比学习模型在检测古建筑物方面实现了79.0%的平衡准确率.
- 在不平衡的卫星图像数据集上,与最先进的方法相比,显示出3.8%的改进.
- 在半监督环境中,成功地利用了标记数据的很小比例 (<7%).
结论:
- 拟议的半监督框架有效地克服了在大规模卫星图像中检测罕见的考古特征的挑战.
- 这种方法为传统调查方法和纯粹监督机器学习提供了更具可扩展性和准确性的替代方案.
- 该方法为通过先进的机器学习技术推进考古勘探提供了有希望的方向.
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