无需手动配准，直接处理大图实现高精度变化检测
Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation
- 通过几何估计自监督训练编码器，实现零样本图像配准
- 直接处理6K×4K大图，复杂场景下仍保持高精度
- 适合真实世界中存在显著形变的遥感图像变化检测
作为地球观测系统中的关键环节，变化检测（CD）旨在揭示观测区域在时空维度上的演变。现有算法通常依赖于多时相图像间精细配准后的地理参考对齐，但在多数真实场景中，需预先进行人工配准，显著增加了工作流复杂度。本文提出一种基于几何估计的自监督变化检测框架——MatchCD。该框架利用零样本能力，通过自监督对比学习优化编码器，其表示可复用于下游图像配准与变化检测，同时解决双时相图像未对齐与目标变化问题。不同于传统方法需将全图分割为小块处理，MatchCD可直接处理原始大尺度图像（如6K×4K分辨率），在多个存在显著几何畸变的复杂场景中均表现出优异性能。
原文摘要 · Abstract (English)
As an essential procedure in earth observation system, change detection (CD) aims to reveal the spatial-temporal evolution of the observation regions. A key prerequisite for existing change detection algorithms is aligned geo-references between multi-temporal images by fine-grained registration. However, in the majority of real-world scenarios, a prior manual registration is required between the original images, which significantly increases the complexity of the CD workflow. In this paper, we proposed a self-supervision motivated CD framework with geometric estimation, called "MatchCD". Specifically, the proposed MatchCD framework utilizes the zero-shot capability to optimize the encoder with self-supervised contrastive representation, which is reused in the downstream image registration and change detection to simultaneously handle the bi-temporal unalignment and object change issues. Moreover, unlike the conventional change detection requiring segmenting the full-frame image into small patches, our MatchCD framework can directly process the original large-scale image (e.g., 6K*4K resolutions) with promising performance. The performance in multiple complex scenarios with significant geometric distortion demonstrates the effectiveness of our proposed framework.
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