提出新框架提升无监督遥感变化检测精度，解决生成器过拟合问题。
Consistency Change Detection Framework for Unsupervised Remote Sensing Change Detection
- 引入循环一致性模块降低生成器过拟合。
- 通过语义一致性模块增强细节重建能力。
- 适合遥感图像变化检测研究者使用。
无监督遥感变化检测旨在无需标注数据的情况下，从同一地理区域不同时相的遥感图像中监测与分析变化。以往方法依赖生成网络进行跨时相图像风格迁移重建，将无法重建区域视为变化区域，但常因生成器过拟合导致性能不佳。本文提出一种新型一致性变化检测框架（CCDF），引入循环一致性（CC）模块以缓解生成器过拟合问题，并设计语义一致性（SC）模块实现更精细的重建。大量实验表明，该方法优于现有最先进方法。
原文摘要 · Abstract (English)
Unsupervised remote sensing change detection aims to monitor and analyze changes from multi-temporal remote sensing images in the same geometric region at different times, without the need for labeled training data. Previous unsupervised methods attempt to achieve style transfer across multi-temporal remote sensing images through reconstruction by a generator network, and then capture the unreconstructable areas as the changed regions. However, it often leads to poor performance due to generator overfitting. In this paper, we propose a novel Consistency Change Detection Framework (CCDF) to address this challenge. Specifically, we introduce a Cycle Consistency (CC) module to reduce the overfitting issues in the generator-based reconstruction. Additionally, we propose a Semantic Consistency (SC) module to enable detail reconstruction. Extensive experiments demonstrate that our method outperforms other state-of-the-art approaches.
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