With the introduction of deep learning methods, the computation required for remote sensing change detection has significantly increased, and distributed computing is applied to remote sensing change detection to improve computational efficiency. However, due to the large size of deep learning models, the time-consuming gradient transfer during distributed model training weakens the acceleration effectiveness in change detection. Data communication and updates can be the bottlenecks in distributed change detection systems with limited network resources. To address the interrelated problems, we propose a communication-efficient distributed deep learning remote sensing change detection framework (CEDD-CD) based on the synchronous update architecture. The CEDD-CD integrates change detection with communication-efficient distributed gradient compression approaches, which can efficiently reduce the data volume to be transferred. In addition, for the implicit effect caused by the delay of compressed gradient update, a momentum compensation mechanism under theoretical analysis was constructed to reduce the time consumption required for model convergence and strengthen the stability of distributed training. We also designed a unified distributed change detection system architecture to reduce the complexity of distributed modeling. Experiments were conducted on three datasets; the qualitative and quantitative results demonstrate that the CEDD-CD was effective for massive remote sensing image change detection.

中文翻译：

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一种高效通信的分布式深度学习遥感图像变化检测框架

随着深度学习方法的引入，遥感变化检测所需的计算量显着增加，分布式计算应用于遥感变化检测以提高计算效率。然而，由于深度学习模型规模较大，分布式模型训练过程中耗时的梯度转移削弱了变化检测的加速效果。数据通信和更新可能是网络资源有限的分布式变更检测系统的瓶颈。为了解决相关问题，我们提出了一种基于同步更新架构的通信高效的分布式深度学习遥感变化检测框架（CEDD-CD）。 CEDD-CD将变化检测与通信高效的分布式梯度压缩方法相结合，可以有效减少要传输的数据量。此外，针对压缩梯度更新延迟带来的隐性效应，构建了理论分析下的动量补偿机制，以减少模型收敛所需的时间消耗，增强分布式训练的稳定性。我们还设计了统一的分布式变更检测系统架构，以降低分布式建模的复杂性。在三个数据集上进行了实验；定性和定量结果表明CEDD-CD对于海量遥感图像变化检测是有效的。