从分类中定位:用于远程传感图像的自导弱监控对象定位
IEEE transactions on neural networks and learning systems
|September 6, 2023
概括
本研究介绍了一种自我指导的弱监督策略 (SD-WSS),用于远程传感图像中的对象定位. 该方法增强了空间特征提取,并使用了一种新的损失函数来提高准确性,降低注释成本.
科学领域:
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 在远程传感图像 (RSI) 中对象定位和检测对于各种应用至关重要.
- 完全监督的方法需要广泛,昂贵的实例级注释.
- 弱监督对象定位 (WSOL) 使用图像级标签提供了一个具有成本效益的替代方案.
研究的目的:
- 为RSIs中的对象定位提出一种新的自我指导的弱监督策略 (SD-WSS).
- 加强空间特征提取并解决WSOL中的歧视性区域问题.
- 为了减少背景干扰并提高复杂RSIs的定位精度.
主要方法:
- 利用和增强分类模型的空间特征提取能力.
- 使用GradCAM++提取空间位置信息以指导学习过程.
- 设计了一种新的自导损失函数,以优化模型焦点和减轻背景噪声.
主要成果:
- 拟议的SD-WSS方法在与主流WSOL方法相比显示出更高的性能.
- 在新创建的WSOL基准 (C45V2和PN2) 上进行了广泛的实验.
- 该方法有效地定位了感兴趣的对象,尽管RSI的复杂背景.
结论:
- SD-WSS策略在遥感图像中显著提升了弱监督的对象定位.
- 该方法提供了一种切实可行的解决方案,可以减少注释工作,同时保持高局部化准确度.
- 开发的基准和提出的方法有助于推动远程传感领域的WSOL研究.
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