在卫星图像中通过对立类促使弱监督变化检测的推进
概括
弱监督变化检测 (WSCD) 方法与背景噪声作斗争. 我们的Adversarial Class Prompting (AdvCP) 方法有效地将对象变化与背景变化区分开来,大大提高了卫星图像中的检测精度.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 弱监督变化检测 (WSCD) 使用图像级标签进行卫星图像分析,减少注释需求.
- 由于有限的监督,WSCD方法经常将背景变化错误地归类为对象变化.
- 复杂的遥感场景加剧了区分物体变化与背景噪声的挑战.
研究的目的:
- 为了解决低监督变化检测中同时出现的噪音问题.
- 开发一种方法,准确地区分对象的变化和背景变化.
- 提高现有的WSCD技术的性能,而不会增加推断成本.
主要方法:
- 提出两个阶段的对抗性类提示 (AdvCP):对抗性提示挖掘和对抗性样本纠正.
- 敌对的快速挖矿使用错误的标签来揭示被错误地归类为对象更改的背景特征.
- 敌对样本校正将这些样本集成到在线全球原型中,以进行无偏的校正.
主要成果:
- 在ConvNet,变压器和分段任何模型 (SAM) 基线上,AdvCP显著提高了性能.
- 取得了显著的IOU改进:WHU-CD上的7.37%,LEVIR-CD上的7.46%,DSIFN-CD上的6.56%.
- 证明了对其他多类弱监督密集预测任务的概括性.
结论:
- 在WSCD中,AdvCP有效地减轻了背景噪声,提高了准确性.
- 该方法与现有的WSCD方法无集成,提供实际好处.
- 在各种遥感应用中,AdvCP显示出改善变化检测的前景.
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