【SCPFCD】Change Detection Based on SupervisedContrastive Learning for High-ResolutionRemote Sensing

出处：TGARS2023

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摘要：变化检测（CD）是高分辨率双时遥感图像中一项具有挑战性的任务。最近CD任务的许多研究都集中在设计全卷积孪生网络架构上。然而，这些方法中的大多数通过随机值或ImageNet预训练模型来初始化编码器，而没有CD任务的先验知识，从而限制了CD模型的性能。在本文中，提出了一种新的监督对比预训练和微调CD（SCPFCD）框架，该框架由两个级联级组成，用于训练基于预训练编码器的CD网络。在第一个监督对比预训练阶段，孪生网络的编码器在标记的CD数据被要求解决所提出的CD对比预训练方法上引入的联合借口任务。所提出的CDContrast预训练方法包括基于监督对比学习的陆地对比学习（LCL）和代理CD学习。LCL专注于从双时图像中学习土地覆盖之间的空间关系，求解地物对比度任务，而代理CD学习在上采样模块的顶部执行代理CD任务，以避免LCL的局部最优并学习CD的特征。然后，在第二微调阶段，用预训练的编码器初始化的整个孪生网络以端到端的方式微调CD任务。所提出的SCPFCD框架通过三个高分辨率遥感图像的CD数据集进行了验证。大量的实验结果一致表明，所提出的框架可以有效地提高孪生网络提取变化信息的能力。

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【SCPFCD】Change Detection Based on SupervisedContrastive Learning for High-ResolutionRemote Sensing

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