多源远程传感分类分类的分布独立域泛化
IEEE transactions on neural networks and learning systems
|November 12, 2024
概括
一个新的功能分发独立网络 (FDINet) 通过提高概括能力来增强多源遥感跨域分类. 这种方法克服了标准卷积神经网络 (CNN) 在测试数据不可用时的局限性.
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:
- 多源遥感数据使全面的地球观测成为可能.
- 卷积神经网络 (CNN) 整合了特征提取和分类,但由于分布假设,它在域泛化方面遇到了困难.
- 对测试数据的有限访问阻碍了传统的CNN在跨域分类任务中的性能.
研究的目的:
- 开发一种新型网络,即特征分布独立网络 (FDINet),用于强大的多源远程传感跨领域分类.
- 在面对未见的目标域时解决CNN的概括限制,而不需要特征对齐或脱.
- 增强不同数据源和领域之间的协作能力.
主要方法:
- 设计了一个基线网络来提取多源跨域特征,利用浅重量共享网络来捕获共同的线条和纹理特征.
- 集成的模式预测概率来量化源-目标域相似性,从而促进跨域协作.
- 开发了一个敏度感知特征区分 (SAFD) 策略用于模型优化,最大限度地降低局部最佳敏度以提高概括性.
- 引入了歧视约束,以平衡特征歧视和泛化,减轻梯度冲突.
主要成果:
- 与现有方法相比,拟议的FDINet在多源遥感跨领域分类方面表现出优异的性能.
- 在两个数据集上的实验验验证了FDINet在定量和定性分析中的有效性.
- 在不牺牲特征歧视的情况下,SAFD战略成功地提高了模型概括能力.
结论:
- 在多源遥感中,FDINet为跨域分类提供了强大的解决方案,克服了传统CNN的局限性.
- 独立于特征分布的方法和敏度意识的优化是实现强大的概括的关键.
- 这项工作推进了深度学习在遥感中的应用,用于局限或无法访问目标域数据的场景.
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