基于统计差异表示的变压器用于异质变化检测
Xinhui Cao1,2, Minggang Dong3, Xingping Liu4
1School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou 511300, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究引入了一种新的弱监督方法,用于检测异质变化,使用结构相似性生成可靠的标签. 该方法有效地检测来自不同传感器的图像的变化,克服数据限制.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 异质变化检测比较来自不同传感器的图像随着时间的推移.
- 深度学习和域调整方法是当前的主流方法.
- 缺乏可信的标签阻碍了现有方法的实际应用.
研究的目的:
- 为异质变化检测开发一个监管较弱的框架.
- 为了克服标签数据不足的局限性.
- 提高异质图像中变化检测的准确性和稳定性.
主要方法:
- 提出了一个结构相似性引导的样本生成 (S3G2) 策略,用于可靠的伪标签生成.
- 引入了一个统计差异表示变压器 (SDFormer) 来减轻模式差异.
- 雇员差异结构相似性,用于先前获取信息.
主要成果:
- 拟议的S3G2战略反复生成可靠的伪标签.
- SDFormer有效地减少了模态差异在比特时异质图像中的影响.
- 实验结果表明,与最先进的方法相比,其性能具有竞争力.
结论:
- 开发的监管薄弱的框架解决了在异质变化检测中有限的标记数据的挑战.
- 提出的方法显示了对现实世界应用的巨大潜力,这些应用需要从各种图像来源检测变化.
- 对参数影响的进一步调查证实了该方法的稳定性.
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